An Unmanned Aerial Vehicle (UAV) path planning method based on improved Rapidly-exploring Random Tree (RRT) algorithm combined with Artificial Potential Field (APF) and genetic algorithm is proposed to overcome the disadvantages of strong randomness of search range and slow convergence rate.Firstly, the target bias is introduced to guide the generation of random sampling points, and the target points have a certain probability of becoming sampling points, thus reducing the number of samples.Meanwhile, the APF is introduced to improve the generation direction of the new node, and the direction of the resultant force between the target point and obstacles is taken as the growth direction of the search tree, which improves the efficiency of path search.Then, a group of track points generated by the improved RRT algorithm are used as the initial population of the genetic algorithm, and a fitness function model is established.The genetic algorithm is applied to optimize the path, and the optimal path is obtained, which solves the problem of path randomness.Finally, the simulation results show that the path generated by the improved algorithm is shorter in length and consumes less time.