Addressing and mitigating the impact of human development on native ecosystems has recently become a critical and widely adopted strategy in Mexico. The scope and success of such projects strongly relies on the possibility to accurately predict the amount of time and resources needed to complete each restoration, as any unforeseen activities translate directly into economical loses that could jeopardize current and future funding for such projects. This paper presents three mathematical and computational algorithms that were developed to optimize the plant distribution on a restoration project in the Mexican plateau by formulating it as a CVRP (Capacitated Vehicle Routing Problem). The first method uses linear programming and a Mixed Integer Programming (MIP) solver, while the second and third methods use meta-heuristics. The three algorithms showed optimal and computationally efficient results for networks with less than 16 nodes, where each node represents a quadrant to be restored. In the second part of our work, the same algorithms were evaluated in problems with up to 26 nodes, finding that the exact mathematical formulations grow exponentially in execution time. However, the formulation based on meta-heuristics (OR-Tools and KNN) kept the execution time to just over a second for all the cases considered.