The work aims to improve the performance of genetic algorithm (GA) to solve the vehicle routing optimization problem in the package recycling. Based on the improvement of traditional genetic algorithm, the hybrid bee genetic algorithm (HBGA) was put forward. Firstly, the initial population generation method of the traditional GA was improved, and the mixed generation operator of the initial population was designed; secondly, the maximum reservation crossover operator was proposed to protect the excellent sub-path; then, on the basis of the above-mentioned improvement, the bee evolutionary mechanism was introduced to ensure thepopulation diversity and the utilization of the excellent individual characteristic information. Finally, the simulation testswere carried out on a standard example set. Compared with the traditional GA, the HBGA was improved regarding its global optimization ability, algorithmstability and running speed. In addition, the global optimization ability and stability of the HBGA were superior to the particle swarm optimization (PSO) algorithm, ant colony optimization (ACO) algorithm and tabu search (TS) algorithm, but its running speed was slightly slower than the TS algorithm. The improvement of traditional GA is reasonable, and the overall solution performance of HBGA is better than the PSO algorithm, ACO algorithm and TS algorithm.