The growth of renewable energy creates awareness and interest among researchers in optimize demand response of solar energy generation. The aim for this study focuses on optimizing the demand response of solar energy generation using Genetic Algorithm (GA) to minimize daily yield loss caused by load shedding. The growing demand for renewable energy particularly solar power, presents challenges in balancing energy supply and demand due to its intermittent nature and weather. The integration of solar energy into existing power grids is often hindered by fluctuations in solar radiation, unpredictable demand, and inefficiency in energy utilization. Without and effective optimization method, will occurs power instability and performance rate of generated solar energy. To address this issue, a GA-based optimization model was developed to enhance energy generation efficiency by reducing wastage due to load shedding. The methodology involves data collection from UiTM Kampus Dungun’s solar energy project, implementing GA for optimization and evaluating its performance based on fitness value and convergence trends. The algorithm’s objective function minimizes daily loss due to load shedding while ensuring a more balanced and stable energy distribution to end users. The experimental results demonstrate the GA effectively reduces energy loss, achieved approximately 99% improvement in managing demand response. This study infers that GA is a feasible tool in the optimization of energy generation from the sun, therefore contributing to good energy management. Future works include collecting more data for the reference of the model, while its performance later could be compared with other optimization algorithms.