The weighting coefficient matrix in the Linear Quadratic Regulator (LQR) of the active suspension system for vehicles has drawbacks of subjectivity and low efficiency. To address this issue, the paper proposes a weight coefficient matrix strategy based on the Red-billed Blue Magpie optimization algorithm. A 1/4 vehicle model, random road surface and impact road surface model are established. A linear quadratic regulator is designed, and a fitness function is constructed. The optimal weighting coefficient matrix and the minimum fitness function value are obtained through the Red-billed Blue Magpie algorithm in the search space. MATLAB/Simulink is used for simulation and a comparative analysis is conducted on passive suspension systems and active suspension systems under C-class road and impact road excitations, along with other optimization algorithms. The simulation results indicate that this proposed control strategy achieves optimizations of 75.50% for the vehicle body vertical acceleration, 48.23% for the wheel dynamic load and 47.63% for the suspension dynamic travel on Class-C road surface compared with the passive suspension and snake algorithm optimization active suspension control. The first peak value of vertical acceleration on the impact road surface is optimized by 76.80%, enhancing vehicle ride comfort and safety.