Heavy vehicle traffic is one of the main problems in large cities, and in Brazil, where the competent authorities have not trained road networks, overcrowding in traffic causes even more obstacles. The applications of computational intelligence techniques in traffic are very broad, with an emphasis on intelligent traffic lights. For the design of intelligent traffic lights, this work proposes the use of Fuzzy Logic, but its main objective is the automatic generation of Fuzzy systems. In order to achieve this objective, the SUMO traffic simulation software was used, which allowed the development of three intersection scenarios controlled by traffic lights. In these scenarios, the traffic performance was evaluated from different adjustments in the pertinence functions and in the set of rules of the Fuzzy system that controls the traffic lights, and these adjustments were made by the AG (Genetic Algorithm) and PSO (Particle Swarm Optimization) algorithms. When comparing traffic performance with traffic lights controlled by Fuzzy and optimized Fuzzy, there are quite significant improvements in the traffic variables analyzed, such as waiting time and the queue size of cars. Thus, this work shows the importance of using evolutionary Fuzzy models to optimize parameters.