Path tracking is essential for unmanned driving. This study presents the design of a path tracking system for unmanned trucks, aiming to enhance accuracy and stability across various speeds. The system employs a linear quadratic regulator (LQR) optimized through an improved genetic algorithm. Firstly, this study begins by establishing a two-degree-of-freedom dynamic model and tracking error model of the vehicle based on the natural coordinate system. Subsequently, a LQR controller is designed to eliminate steady-state errors and enhance tracking accuracy through the implementation of feedforward control. Secondly, the genetic algorithm is enhanced to optimize the weight matrix of the LQR controller, resulting in improved accuracy and stability for path tracking. Finally, the control effectiveness of the designed LQR controller was simulated and verified across a range of operating conditions using the joint simulation platform of Matlab/Simulink and TruckSim. The findings demonstrate that the tracking accuracy of the LQR controller, optimized using GA, has exhibited an average improvement of approximately 38.8%. Additionally, it has demonstrated enhanced stability. Specifically, the position error and heading error could be maintained within 0.17m and 0.11rad, respectively, thereby validating the efficacy of the tracking control framework proposed in this research paper.