To improve the problems of poor real-time performance and inability to automatically adjust parameters of the existing anti-lock braking system (ABS) using proportional integral differential (PID) control method, a PID control method for anti-lock braking system based on multi-strategy aquila optimizer (AO) is proposed. Taking a single-wheel vehicle model as an example, firstly, the PID controller simulation model of the vehicle anti-lock braking system is constructed. Secondly, a differential evolution combining reverse learning combined with stagnation perturbation for aquila optimizer (DERLSP-AO) is proposed to overcome the limitations of the standard AO, particularly its tendency to converge to local optima and its limited search precision. By designing the reverse learning strategy of hunting perspective to increase the search range, the efficiency of the algorithm is improved. Additionally, a differential evolution strategy was integrated to evolve the aquila population by eliminating weaker individuals. By mixing multiple strategies, the DERLSP-AO method design is completed. Then, the optimal individual tuning PID parameters are used to obtain the optimized DERLSP-AO-PID controller. Finally, different road conditions are selected to simulate the anti-lock braking process of the vehicle. The results show that, compared to existing algorithms, the slip rate curve of the ABS output based on DERLSP-AO-PID control shows improved performance in maintaining the desired range. The vehicle exhibits reduced braking time and shorter stopping distances, which further validates the effectiveness of the improved algorithm and demonstrates enhanced braking performance.