To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers. Achieve one-time distribution of the front axle braking force; the second layer, according to the magnitude of the braking intensity, divides the braking conditions into light braking, moderate braking and emergency braking, and realizes braking under the three working conditions respectively. Secondary distribution of front axle braking force. Using the driving mileage contribution as the evaluation index and NEDC as the simulation working condition, it is verified that the regenerative braking control strategy can achieve energy recovery. To further improve the braking energy recovery and ensure the vehicle braking effect, the braking effect and braking energy recovery are used as the optimization objective function, the particle swarm algorithm is used to optimize the fuzzy rules, and the optimized fuzzy controller is reloaded into the regenerative braking control Simulation is carried out in the strategy to verify that the designed multi-section front and rear wheel braking force distribution curves and fuzzy rules optimized by particle swarm algorithm can effectively improve regenerative braking energy recovery and improve vehicle driving range, while ensuring braking safety.