We introduce a design procedure for fuzzy systems using the concept of information granulation and genetic optimization. Information granulation and resulting information granules themselves become an important design aspect of fuzzy models. By accommodating the formalism of fuzzy sets, the model is geared towards capturing relationship between information granules (fuzzy sets) rather than concentrating on plain numeric data. Information granulation realized with the use of the standard C-Means clustering helps determine the initial values of the parameters of the fuzzy models. This in particular concerns such essential components of the rules as the initial apexes of the membership functions standing in the premise part of the fuzzy rules and the initial values of the polynomial functions standing in the consequence part. The initial parameters are afterwards tuned with the aid of the genetic algorithms (GAs) and the least square method (LSM). The overall design methodology arises as a hybrid development process involving structural and parametric optimization. Especially, genetic algorithms and C-Means are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we exploit the methodologies such as joint and successive method realized by means of genetic algorithms. The proposed model is evaluated using experimental data and its performance is contrasted with the behavior of the fuzzy models available in the literature.