The community detection in dynamic networks is essential for important applications such as social network analysis. Such detection requires simultaneous maximization of the clustering accuracy at the current time step while minimization of the clustering drift between two successive time steps. In most situations, such objectives are often in conflict with each other. This article proposes the concept of consensus community. Knowledge from the previous step is obtained by extracting the intrapopulation consensus communities from the optimal population of the previous step. Subsequently, the intrapopulation consensus communities of the previous step obtained is voted by the population of the current time step during the evolutionary process. A subset of the consensus communities, which receives a high support rate, will be recognized as the interpopulation consensus communities of the previous and current steps. Interpopulation consensus communities are the knowledge that can be transferred from the previous to the current step. The population of the current time step can evolve toward the direction similar to the population in the previous time step by retaining such interpopulation consensus community during the evolutionary process. Community structure is subjected to evaluation, update, and mutation events, which are directed by using interpopulation consensus community information during the evolutionary process. The experimental results over many artificial and real-world dynamic networks illustrate that the proposed method produces more accurate and robust results than those of the state-of-the-art approaches.