This work proposes a multi-objective evolutionary constrained clustering (MOECC) approach that leverages the strengths of multi-objective evolutionary algorithms (MOEAs) to efficiently explore trade-offs between clustering quality and constraint adherence. Using MOEA/D, our method decomposes the problem into multiple subproblems, optimizing them simultaneously while maintaining diversity across the solution space. We evaluated MOECC on 31 benchmark datasets and compared its performance with state-of-the-art constrained clustering techniques. The results demonstrate that our approach consistently achieves superior clustering quality while minimizing constraint violations, particularly in scenarios with limited prior information. These findings highlight the effectiveness of MOECC in generating diverse, high-quality clustering solutions that adapt to different constraint levels and data characteristics.