The number of communities and temporal smoothness are always the main limitations in the field of evolutionary community detection for dynamic networks. In this paper, a new multi-objective approach based on the label propagation algorithm (LDMGA) is proposed. Employing the idea of multi-objective genetic algorithm, the evolutionary clustering algorithm is transformed into a multi-objective optimization problem, which not only improves the clustering quality, but also minimizes clustering drift from one time step to the successive one. Population initialization based on the label propagation algorithm improves the clustering quality of initial individuals. In addition, applying the label propagation algorithm to the mutation progress enhances the quality of clustering and the convergence rate. At the same time, the combination of the multi-objective genetic algorithm and the label propagation algorithm makes the algorithm more scalable, and the running time increases linearly with the increase of the number of nodes or edges. The experiment on the synthesized datasets and real datasets shows that the proposed algorithm consistently provides good clustering quality and scalability.