To efficiently solve Vehicle Routing Problems with Time Windows （VRPTW）， a Hybrid Particle Swarm Optimization （HPSO） algorithm was proposed. This algorithm replaced the traditional particle update method with Partially Matched Crossover （PMX）， enhanced diversity by combining the worst neighbor particle selection and roulette wheel selection mechanism， and balanced global exploration and local exploitation capabilities through a dynamic weight adjustment strategy. A Variable Neighborhood Search （VNS） integrating 2-opt inversion， sequential insertion， and swap operations was designed to optimize solution quality， and a greedy algorithm was used to quickly generate high-quality initial solutions. Experimental results on the Solomon standard test set show that the HPSO algorithm has the solution gap within 1% with the known optimal solution for 69% of the test problems in datasets with 25 and 50 customers， and has the solution almost close to the optimal solution for C-class test problems with 100 customers， demonstrating its effectiveness and competitiveness in solving complex VRPTW. On datasets with 100 customers， compared with the Neighborhood Comprehensive Learning Particle Swarm Optimization （N-CLPSO） algorithm， the HPSO algorithm reduces the standard deviation by at least 2.4% on the RC102 test problem， and improves the convergence speed by an average of 41% （59% and 23%） on the C101 and R101 test problems. Through the collaborative optimization of multiple strategies， the HPSO algorithm can significantly improve the solution accuracy， convergence efficiency， and robustness of complex VRPTW.