This chapter explores load frequency control (LFC) in two-area power systems using a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) approach. As modern power systems are affected by renewable energy sources and fluctuating loads, advanced control strategies are necessary for frequency stability. The hybrid GA-PSO method combines GA’s global search ability with PSO’s fast convergence to optimize control parameters, ensuring efficient frequency regulation. The system model includes operational constraints and dynamic characteristics, while the hybrid algorithm fine-tunes proportional-integral-derivative (PID) controllers for quick load response. Simulations under various conditions show the hybrid GA-PSO outperforms traditional methods, achieving faster settling times, minimizing oscillations, and maintaining stability. Compared to traditional methods, which rely on fixed controller gains, the hybrid GA-PSO dynamically adapts to varying conditions, effectively handling uncertainties, load disturbances, and renewable energy integration.