Network traffic classification is elementary to network security and management. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The Gaussian Mixture Model (GMM) based on the correlation of flows has exhibited superior classification performance. It also has several important advantages, such as robust to distributional assumptions and adaption to any cluster shape. However, the performance of GMM can be severely affected by the number of clusters. In this paper, we propose the minimum description length (MDL) criterion which can balance the accuracy and complexity of the classification model effectively by evaluating the optimal number of clusters. We establish a new classification model and analyze its performance. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results demonstrate the efficiency of our approach.