When particle swarm optimization (PSO) is used to solve multiobjective optimization problems, the PSO has a fast convergence effect, which makes the diversity of population in the optimization process insufficient and makes the algorithm converge early. In order to effectively design multiobjective particle swarm optimization algorithm, a multiobjective particle swarm optimization (MOPSO) algorithm based on adaptive mesh mixing mechanism is proposed. The algorithm adopts a dual maintenance strategy of adaptive grid and mixing mechanism to ensure the uniform distribution of noninferior solutions in the external archive, and avoid rapid population degradation and affecting the development ability of particles. The weighted strategy in the mixing mechanism is used to determine the global optimal sample in the external noninferior solution, which increases the diversity of population and improves the probability of particles flying to the real Pareto frontier. At the same time, in order to prevent the algorithm from stagnating and falling into the local optimal problem, a mutation operation is introduced to dynamically change the position of particles, which enhances the exploration ability of particles. The simulation results show that the proposed algorithm has better convergence and diversity and better spatial effect than the other three classical multiobjective particle swarm optimization algorithms.