The main topic of this thesis is the Vehicle Routing Problem (VRP). In particular, four algorithms are proposed to solve the static version of VRP. Each of these four algorithms is characterized by an initial phase in which customers are divided in clusters, prior to the routing phase in which the VRP is actually solved. Two different clustering approaches for the management of customers locations (i.e., the k-means method and the affinity propagation method) and two different resolution methods (i.e., the insert method and the rebuild method) for the planning of routes have been implemented, giving rise to four different Python-based algorithms to solve the VRP.