A new hybrid simulation method for structural reliability analysis is proposed which combines uniform design (UD) technique, artificial neural network (ANN) based meta-model and genetic algorithms (GA).The uniform design instead of classical central composite design (CCD) or orthogonal array design (OAD) is applied to choose experiment points in the space of basic random variables aimed to minimize the number of simulation and to fill the space more uniformly.A BP-ANN based meta-model is used as a smart response surface surrogate to the original implicit limit state function in the global random variable space, with the UD experimental points as input training data sets of the ANN.Due to the highly nonlinear nature of ANN-based smart response surface, the genetic algorithms (GA) are adopted to search the global design point or most probable point (MPP) of failure to avoid fall into the local optimal solutions.To implement deterministic finite element analysis in the evaluation of the limit state function and finite element response sensitivity, the proposed approach is programmed in MATLAB by calling and integrating the commercial finite element analysis program ANSYS.A five-story three-bay steel frame is taken as an application example to demonstrate the accuracy, efficiency and applicability of the proposed method by contrasting the new approach with the classical computational reliability methods such as Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM).