The development of control systems based on fuzzy rules facilitates the solving of problems when insufficient phenomenological information is available. The most common way of grouping fuzzy rules to form a controller is known as Mamdani controller. This controller consists of a set of rules with two premises, the error and the error variation, and one conclusion, the control action variation. One of the most delicate phases of the project of fuzzy systems is the definition of the supports (range) of each fuzzy qualifiers. This work apply genetic algorithms, together with some model of the system, to the adjustment of the supports of the fuzzy sets used in a Mamdani controller. The results show that the automatic adjustment is faster and more efficient that the manual one. Finally, the results are compared with a PID that was also adjusted with genetic algorithms.