Abstract: The usefulness of genetic algorithm-backpropagation(GA-BP) Bayesian algorithm was studied and evaluated for reliability simulation. GA-BP Bayesian algorithm is a algorithm to train feedforward neural networks, and it is based on GA, L-M (Levenberg-Marquardt) BP, and Bayesian method. The algorithm trains a network with the purpose of obtaining the weights corresponding with maximum posterior probability, and it adopts genetic algorithm in searching process. As a result, it makes neural networks have better and steadier generalization ability. When running a reliability simulation, GA-BP Bayesian algorithm can be utilized to train neural networks to make an approximation model that can be used in Monte Carlo simulation instead of expensive numerical program. In this way, the probability distribution of random ouput variables can be obtained with efficiently-controlled computing cost.