As a useful alternative of Shewhart control chart, exponentially weighted moving average (EWMA) control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA (MEWMA) control charts, in which the optimal parameter pair ( λ ,k) or ( λ ,h) was searched by using the generalized regression neural network (GRNN). The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length (ARL0) and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported. As a useful alternative of Shewhart control chart, exponentially weighted moving average (EWMA) control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA ( MEWMA) control charts, in which the optimal parameter pair (λ, k) or (λ, h) was searched by using the generalized regression neural network (GRNN). The results indicate that the optimal parameter pair can be obtained effectively effectively by the proposed strategy for a given in-control average running length (ARL0) and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.