Abstract: Decision tree is a good classifier with transparent decision mechanism. Decision tree building methods usually have problems because of the nature of the tree to split the learning samples to more subsets. If the classification for such a subset is not possible it's better to put off the decision on classification to some other classifier. This leads to introduction of a null classification which simply means that no classification is possible in this step. This approach is sensible with evolutionary methods as they can handle a number of trees simultaneously. In the process of construction we have to address the problem if a classification is sensible. Performance of the proposed model has been tested on several datasets and presented results on one such dataset show its potential.