Abstract: An approach of UAV(unmanned aerial vehicle) path planning based on the improved genetic algorithm CHC(cross generation heterogeneous recombination cataclysmic mutation) algorithm was proposed. The initial population was produced by the searching algorithms containing heuristic information. The fitness function was the combination of distance and menace index. The select operating population was summation of current and previous one, for the operating population was big, the genetic diversity could be kept better. The single node crossover was used in cross operating, and the nearest two ones of two lines were choosen as the cross nodes. The mutate operating process was that two nodes were searched first, then the ratio of the two nodes′ linear distance to the two nodes′ real distance along the path was calculated. If the ratio was less than a fixed value then a new route between the two nodes was planned. Due to the restrict of the UAV′s capability, the algorithm can avoid blindness and can speed up the constringency. Simulation results show that the planning algorithm is faster than the basal genetic algorithms and meet the optimal requirements.