This thesis proposes several algorithms to solve the vehicle routing with time windows static and dynamic versions. These problems involve determining minimum cost routes for a homogeneous fleet in order to meet the demand of a set of customers within specified time intervals popularly called time windows. In addition, in the dynamic version of the problem, new customers can be assigned to vehicles during the execution of the routes. For the static version it was proposed a hybrid algorithm using ant colony optimization and the random variable neighborhood search method. The computational results show that the algorithm was able to find very good or even the best known solutions to benchmark instances. For the dynamic version it was proposed six algorithms, based on an insertion procedure, ant colony optimization and random and sequential versions of variable neighborhood search methods. Computational results show that most of the proposed algorithms are competitive regarding the state of the art, providing solutions of good quality with low computational effort.