Abstract: A switching strategy based on (CPSO-SGA) was presented by combining their chaos particle swarm optimization own advantages. In the switching and specialized genetic algorithm strategy, CPSO is applied in the former step and SGA is executed in the later step. The best switching conditions under three switching indices of iteration steps, population standard deviation, and optimal individual fitness values were determined by large amounts of simulation experiments. In comparison with single SGA and single CPSO, the proposed switching strategy CPSO-SGA has a better performance when path length, smoothness, and running time are taken into consideration.