Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized groundwater level monitoring networks are rarely designed to build up a strong groundwater level data base, and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation(GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology(LiRDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System(GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyses have been performed separately for all kriging methods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyses conclude that the exponential semivariogram model obtained from ordinary kriging gives the best fit model. Study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering multiple parameters such as GWLF and LiRDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area. Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many established countries, optimized groundwater level monitoring networks are rarely designed to build a strong groundwater level data base , and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation (GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology (LiRDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System (GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyzes have been performed separately for all kriging met hods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyzes conclude that the exponential semivariogram model obtained from ordinary kriging gives the study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering several parameters such as GWLF and LiRDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area.