This study proposes MGO-GA, a hybrid optimization approach that combines Genetic Algorithm (GA) with Moss Growth Optimization (MGO). In complex search environments, MGO often suffers from premature convergence and stagnation, despite its great exploration and exploitation capabilities. The suggested approach improves population variety and speeds up convergence by introducing genetic operators crossover and mutation into the MGO process to get around these problems. By using GA's global search ability, the hybrid MGO-GA enhances the results generated by MGO and reduces the possibility of being trapped in local optima