Aiming at the shortcomings of the existing anti-lock braking system (ABS), such as insufficient dynamic performance and excessive dependence on expert experience, a fuzzy PID control method of vehicle ABS optimized by a multi-strategy Aquila algorithm was proposed. A dynamic performance simulation platform of automobile ABS was built, including the vehicle model, brake model and controller model. An improved Aquila optimization algorithm (DEFFAO) based on multi-strategy was proposed to address the issues of the Aquila algorithm easily falling into local optima and having limited search precision. A free hunting strategy for the Aquila was designed to enhance the individual search ability and avoid the algorithm falling into the local optimal value. A nonlinear convergence factor was designed to balance the global and local search capabilities. Combined with the differential evolution strategy, the poor individuals in the Aquila population were eliminated. By mixing a variety of strategies, the DEFFAO method design was completed. A DEFFAO-fuzzy PID controller was obtained by off-line optimization tuning of the scale factor parameters. Different road conditions were selected to simulate the anti-lock braking process of the vehicle. Results show that compared with the existing algorithm, the slip ratio curve output by the ABS based on DEFFAO-fuzzy PID control is better maintained within the expected range, the vehicle braking time is faster, and the braking distance is shorter.