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This paper presents a novel classification approach that integrates fuzzy classification rules and Genetic Network Programming (GNP). A fuzzy discretization technique is applied to transform the dataset, particularly for dealing with quantitative attributes. GNP is an evolutionary optimization technique that uses directed graph structures as genes instead of strings and trees of Genetic Algorithms (GA) and Genetic Programming (GP) respectively. This feature contributes to creating quite compact programs and implicitly memorizing past action sequences. Therefore, in the proposed method, taking the GNP's structure into account 1) extraction of fuzzy classification rules is done without identifying frequent itemsets used in most Apriori-based data mining algorithms, 2) calculation of the support, confidence and x2 value is made in order to quantify the significance of the rules to be integrated into the classifier, 3) fuzzy membership values are used for fuzzy classification rules extraction, 4) fuzzy rules are mined through generations and stored in a general pool. On the other hand, parameters of the membership functions are evolved by non-uniform mutation in order to perform a more global search in the space of candidate membership functions. The performance of our algorithm has been compared with other relevant algorithms and the experimental results have shown the advantages and effectiveness of the proposed model.