Unmanned aerial vehicle (UAV) trajectory planning is to plan a safe and feasible track under the environmental threats and self-constraints. It is one of the key technologies to realize the autonomous flight of an UAV. In order to quickly plan a safe and reliable UAV path in the complex urban environment, this paper presents a hybrid adaptive particle optimization with differential evolution and minimum snap (APSODE-MS) for the UAV path planning in the city. Firstly, this paper establishes a mathematical model for urban environmental trajectory planning, and the weighted sum of flight distance, threat constraint, and violation constraint cost is taken as the objective function. Secondly, the adaptive nonlinear inertia weight is introduced into the PSO algorithm, and different search modes are assigned according to the degree of deviation of the particles from the global optimal solution. The dynamic DE algorithm is used to accelerate the convergence rate of the particles, and the improved normal perturbation is introduced to improve the ability to break out of stagnation and precocity. Finally, the key track points are screened, and the minimum snap(MS) algorithm is used to smooth the track. The simulation results show that the proposed APSODE-MS path planning method can complete the planning task well and obtain a better path in different city simulation environments, thus verifying the effectiveness and robustness of the algorithm.