This dissertation deals with the research, simulation and optimization of the structure of Multilayer Organic Light Emitting Diodes (ML-OLEDs) by using Computational Intelligence techniques. In addition, one of these methods, called Ant Colony Optimization (ACO), was implemented based on a model proposed in the literature and applied for the first time in the optimization of organic diodes. OLEDs are nanometric optoelectronic devices fabricated from organic semiconducting materials. Unlike traditional technologies, they combine high luminance and low power consumption. In the manufacturing of an OLED, the number of possible configurations is almost unlimited due to the number of parameters that can modified. Because of this the determination of the optimal architecture becomes a non-trivial task. Two different simulation models were used to simulate the OLEDs. Thus, the Artificial Neural Networks (ANN) were employed in order to work as the proxy of the commercial simulator and to accelerate the calculation of the current density. The Genetic Algorithms (GA) were applied to determine the optimal values of thickness of the layers, the charge carrier mobility and the concentration of the organic materials in OLEDs with two layers, while the ACO was applied to find the values of concentration in OLEDs with two and five layers, thus establishing three case studies. The employed strategy has proved to be promising, since it has show good results for two case studies, especially for the optimization of the thickness, where there was an experimental confirmation of the bilayer device.