The performance of an indoor positioning system is highly related to the placement of the transmitting nodes that are used as references for the positioning estimations. In this paper, we propose a methodology that can be used to optimize such a deployment and thus, increase the performance of an indoor positioning system that a) is based on Received Signal Strength (RSS) fingerprinting and b) is orientated towards providing location or zone estimations instead of exact positioning. The optimization process involves 4 fundamental components. Firstly, the modelling of the obstructions in the indoor environment and also the zone modelling. Then, the definition of the performance metric that can be used to evaluate each different deployment scenario, in which case, our proposed metric considers the separation area and distances between the zones in the RSS vector space. The third component is the radio propagation model, required for simulating the RSSs from each node, where a model based on the ray tracing technique is selected. Finally, the last component is the selection of the optimization function that will control and drive the whole optimization process by selecting which deployment schemes to evaluate. For that, the utilization of a Genetic Algorithm is proposed. Although the evaluation of this methodology is outside the paper's scope, the key factors affecting the optimization performance the most, are expected to be a) the accuracy of the used indoor model and radio propagation model and b) the exact implementation of the optimization function.