Decision trees as one of the data mining techniques, is used in credit scoring of bank customers. The main problem is the construction of decision trees in that they can classify customers optimally. This paper proposes an appropriate model based on genetic algorithm for credit scoring of banks customers in order to offer credit facilities to each class. Genetic algorithm can help in credit scoring of customers by choosing appropriate features and building optimum decision trees. Development process in pattern recognition and CRISP process are used in credit scoring of customers in construction of this model. The proposed classification model is based on clustering, feature selection, decision trees and genetic algorithm techniques. This model select and combine the best decision tree based on the optimality criteria and constructs the final decision tree for credit scoring of customers. Results show that the accuracy of proposed classification model is more than almost the entire decision tree models compared in this paper. Also the number of leaves and the size of decision tree i.e. its complexity is less than the other models.