Brazil is one of the world s leading producers and exporters of pulp and paper, benefiting from favorable climatic and soil conditions, coupled with substantial investments in research. A significant challenge in this sector is the Forest Harvesting Planning Problem (FHPP), akin to a derivative of the Vehicle Routing Problem (VRP) featuring a heterogeneous fleet, periodic demand, and wood volume gain. This study addresses FHPP by employing Mixed Integer Linear Programming (MILP) modeling and the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic across real and simulated scenarios to optimize the sequencing of harvesting teams among stands. The objective is to reduce operational costs and enhance volume growth over a 12- month planning horizon, while also considering time windows and scheduling constraints. A total of 12 instances were tested to evaluate GRASP s performance, with the metaheuristic matching or outperforming the MILP model in nine cases. Additionally, three instances reflect real scenarios from a major Brazilian pulp and paper company. When compared against the company s planning team results, GRASP achieved up to a 61.9 percent reduction in total costs. Furthermore, GRASP provides detailed harvesting plans within a short execution time, reducing planning team workload and enhancing decision-making flexibility.