Decision trees technique as one of the data mining techniques, is used in credit scoring of bank customers to classify them in order to offer credit facilities. The main problem is in complexity of decision trees, excessive size, lack of flexibility and low accuracy in classification. The purpose of this paper is to propose a compound model in the optimization of decision trees by using genetic algorithm technique. It appears that genetic algorithm can choose appropriate features and build decision trees to reduce complexity and increase flexibility in decision trees. In the proposed compound model, the credit data is initially divided into two clusters by Simple means clustering technique. On the next step, the important credit scoring features in the data set are selected using genetic algorithm and the five feature selection algorithm based on Filter, Wrapper and Embedded approaches. Subsequently, five decision trees based on C4.5 algorithm in each cluster are constructed with a set of the selected features.The best decision trees in each cluster, are selected and combined based on the desired optimality criteria, mentioned in this paper, to construct the final decision tree. WEKA machine learning tool and GATree software were used to in this purpose. Results show that using the proposed compound model in building decision trees leads to increased classification accuracy, compared to other algorithms in this paper. However the algorithm complexity of the proposed compound model is more than some of the classification algorithms compared in this paper.