The thesis work entails the design of a Bacterial Memetic Algorithm (BMA)-optimized Linear Quadratic Regulator (LQR) controller for a vehicle suspension system. The BMA combines and integrates two optimization methods – the Bacterial Foraging Optimization Algorithm (BFOA) and the Memetic Algorithm (MA), thereby enhancing its search efficiency and reducing the risk of getting stuck in local minima. Through this synergistic integration, an optimal feedback gain for the LQR controller is determined, which in turn controls the random vibration and oscillation of the suspension system, thereby enhancing comfort, safety, and road handling. The MATLAB 2024b environment was utilized for the simulation. The Fast Fourier Transform (FFT) was used to calculate the Power Spectral Density (PSD) of the randomly generated road profile applied in the study. The results of the proposed model were compared with two other models – the Genetic Algorithm (GA)-LQR and the Virus Evolutionary Genetic Algorithm (VEGA)-LQR optimized models – to demonstrate the efficiency and efficacy of the proposed model.