Aiming at the problem that the dung beetle optimization algorithm (DBO) has insufficient global exploration ability and easily falls into the local optimum, resulting in poor UAV 3D trajectory planning, an improved dung beetle optimization algorithm (EDBO) is designed. Firstly, a leader dung beetle group incorporating the idea of a grey wolf leader group is divided from the stealing dung beetle population to enhance the diversity and robustness of the algorithm; secondly, a population switching strategy is used, where the behaviour of dung beetle individuals with the same number is no longer fixed to improve the algorithm′s ability of global exploration and local mining; and lastly, a Beta-distributed dynamic inverse learning strategy is used to help dung beetles to evolve better. The proposed algorithm is compared with 4 optimization algorithms for UAV 3D trajectory planning, and the simulation results show that EDBO can generate trajectories with smaller cost function values more stably in the 3 scenarios, and the average cost function values are reduced by 9.8%, 10.4%, and 16.5% compared to the original DBO algorithm, respectively.