Reforestation plays a crucial role in mitigating climate change and restoring ecological balance [5]. However, high interspecific competition among planted species can significantly affect survival rates and ecosystem resilience. This study addresses the problem of minimizing inter-species competition by optimizing the spatial allocation of tree species in reforestation projects. An exact mathematical programming model was initially developed to determine optimal planting configurations based on species compatibility and resource competition. While the model produced satisfactory solutions for small instances, it exhibited severe computational limitations for larger problem sizes. To overcome these challenges, a Genetic Algorithm (GA) was implemented as a metaheuristic alternative. The GA demonstrated strong scalability, consistently generating feasible and competitive solutions across all problem sizes, including large-scale instances involving hundreds of planting positions and multiple species. Comparative analysis shows that the GA outperforms the exact model in terms of computational efficiency while delivering high-quality solutions. The results support the application of evolutionary algorithms for practical reforestation planning, particularly when exact optimization becomes computationally intractable.