This dissertation investigates the use of genetic programming in automatic synthesis of assembly language programs for microcontrollers, which implement time-optimal or sub-optimal control strategies of the system to be controlled, from the mathematical modeling by dynamic equations. One of the issues faced in conventional design of an optimal control system is that solutions for this kind of problem commonly involve a highly nonlinear function of the state variables of the system. As a result, frequently it is not possible to find an exact mathematical solution. On the implementation side, the difficulty comes when one has to manually program the microcontroller to run the desired control. Thus, the objective of this work was to overcome these difficulties applying a methodology that, starting from the mathematical modeling of a plant, provides as result an assembly language microcontroller program. The work included a study of the possible types of genetic representation for the manipulation of assembly language programs. In this regard, it has been concluded that the linear is the most suitable representation. The work also included the implementation of a tool to accomplish three study cases: water bath, cart centering and inverted pendulum. The performance of control of the synthesized programs was compared with the one obtained by other methods (neural networks, fuzzy logic, neurofuzzy systems and genetic programming). The synthesized programs achieved at least the same performance of the other systems, with the additional advantage of already providing the solution in the final format of the chosen implementation platform: a microcontroller.