Networks and graphs arise naturally in many complex systems. Often times they exhibit a dynamic behavior that can be modeled using dynamic networks. Two major research problems in dynamic networks are 1) community detection, which aims to find specific sub-structures within the networks, and 2) change point detection, which tries to find the time points that the sub-structures change. This project proposes a new methodology to solve both problems simultaneously, by casting this as a model selection problem and utilizing the Minimum Description Length Principle (MDL) as the minimizing objective criterion. The derived detection algorithm is compatible with many existing methods, and is supported by empirical results and data analysis.