Typically, self-organizing systems comprise of a large number of individual agents whose behavior needs to be controlled by set parameters so that their interactions lead to the creation of the desired system. To be self-organizing, the system must mimic the evolutionary process. One way to do this is by use of an evolutionary algorithm. This mimics naturally-occurring genetic variation (mutation and recombination of genes). To fulfill this purpose, we have created a tool named FREVO (FRamework for EVOlutionary design), which separates the input needed into the following components: target problem evaluation, controller repre- sentation and the optimization method. FREVO provides well-defined interfaces for these components and supports a graphical user interface to simulate the evolutionary process. After obtaining the outcome for a simulation, it is possible to validate and evaluate the results within FREVO. FREVO has been successfully applied to various problems, from cooperative robotics to economics, pattern generation and wireless sensor networks. In this paper, we give an overview of the architecture of FREVO and introduce a case study involving smart grid networks.