Team Orienteering Problem is a vehicle routing problem on a graph with durations associated to the arcs and profits assigned to visiting the vertices. In this problem, a fleet with a fixed number of identical vehicles performs the visitations and there is a limited total duration for the routes to be ended up. Each vertex can be visited at most once and the solution does not have the obligation to visit all vertices, due to the constraint that limits the maximum duration of routes. The goal of the problem is to maximize the total profit gathered by all routes. In this work, two approaches have been proposed: an exact and a heuristic one. In the exact approach, we have developed an arc based formulation and an extended formulation where each arc has an extra index. This index represents the departure time of a vehicle using an arc. Through transformations on the extended formulation, we have obtained a formulation, whose relaxation - the restricted master problem - is solved using the column generation technique. A dynamic programming algorithm solves the column generation subproblem in pseudo-polynomial time. This algorithm generates non-elementary routes that allow subcycles. In order to cut off the subcycles, a new class of inequalities called min cut has been proposed.We have applied a Branch-Cut-and-Price (BCP) algorithm. This allowed finding some new upper bounds. The exact approach has achieved competitive results compared to the best exact algorithm has already proposed to this problem. In the heuristic approach, besides a kopt neighborhood, we have also exploited an ellipsoidal search that adds a new cut constraint to the formulation of Branch-Cut-and-Price algorithm. This new cut reduces the space search to a neighborhood around a known set of solutions. This search is used as a crossover operator that runs all iterations of a evolutive algorithm. This approach converges in a reasonable computational time and finds optimal or near optimal solutions for some instances in the literature.