Natural resource management involves complex decision-making processes that often result in non-linear optimization problems. This study explores the application of genetic algorithms (GA) and particle swarm optimization (PSO) to manage resources like water and forest reserves more efficiently. We compare the effectiveness of these algorithms in achieving sustainable utilization while minimizing environmental impact. The results show that GA outperforms PSO in forest management scenarios, while PSO is more suitable for water resource distribution.