Abstract: To solve the problems of slow convergence speed and tendency to fall into the local optimum of the standard particle swarm optimization while dealing with nonlinear constraint optimization problem,a particle swarm genetic algorithm is designed.The proposed algorithm adopts feasibility principle handles constraint conditions and avoids the difficulty of penalty function method in selecting punishment factor,generates initial feasible group randomly,which accelerates particle swarm convergence speed,and introduces genetic algorithm crossover and mutation strategy to avoid particle swarm falls into the local optimum.Through the optimization calculation of the typical test functions,the results show that particle swarm genetic algorithm has better optimized performance.The algorithm is applied in nuclear power plant optimization,and the optimization results are significantly.