The work aims to more rationally carry out vehicle routing management, and improve the performance of particle swarm optimization to solve the problem of vehicle routing optimization. A particle swarm optimization algorithm based on dynamic monkey jumping mechanism was proposed. By means of the dynamic grouping of groups, different dynamic inertia weights were used to improve the speed of the algorithm. Monkey jumping mechanism was introduced to ensure global convergence. Finally, the improved algorithm was applied to two examples of logistics distribution path optimization. Under the same environment, the number of successful cases that the improved algorithm found the optimal path adaptation value and the average operation time and obtained the optimal solution was better than the standard particle swarm optimization algorithm. The results showed that, the improved algorithm could quickly and efficiently determine the logistics distribution path. The improved particle swarm optimization algorithm not only has faster speed of optimization, but also improves the convergence of the algorithm and ensures the optimization quality; therefore, it has great application value.