Abstract: A new method of designing BP neural networks based on immune genetic algorithm (IGA) was proposed. The mechanisms of diversity maintaining and antibody density regulation exhibited in a biological immune system were introduced into IGA based on genetic algorithm (GA). The proposed algorithm overcame the problems of GA on search efficiency, individual diversity and premature, and enhanced the convergent performance effectively. In order to solve the problem of random initial weights, simulated annealing algorithm for diversity was used to initialize weight vectors, and the detailed design steps of the algorithm were given. Simulated results show that the BP neural networks designed by IGA have better performance in Convergent speed and global convergence compared with hybrid genetic algorithm.