This work present offline optimization strategy to boost the performance of model predictive control in automotive lateral dynamics by using the dynamic differential annealed optimization algorithm. Because real-time optimization can be both complex and time-consuming, the proposed approach focuses on fine-tuning key MPC parameters—such as prediction horizon, control horizon, and weighting factors for manipulated variables and output deviations—to achieve better control outcomes. This paper use model predictive control toolbox in MATLAB to explore these parameters thoroughly, ensuring finding the best possible settings. Simulation results show that our optimized controller greatly improves the ability to follow a desired path, reducing lateral position error to a minimal level. The optimization-derived parameters also produce notably lower fitness values, indicating better control accuracy and overall stability. Overall, this method provides a solid framework for designing control strategies that prioritize safety, comfort, and performance in a range of driving conditions. Our findings highlight how effective optimization techniques can move automotive control systems forward.