This paper presents several strategies for parallel implementations of the greedy randomized adaptive search procedure (GRASP) and the variable neighborhood search (VNS) applied to a combinatorial optimization problem known as the traveling purchaser problem (TPP). Parallel algorithms based on master-worker, completely distributed and independent models, using static and dynamic load balance were proposed. The performance of these parallel algorithms was analyzed comparing them among themselves and with their sequential versions.