The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) has emerged as a robust and computationally efficient framework for addressing complex optimization challenges. In recent years, there has been a significant focus on adapting MOEA/D to effectively tackle constrained multi-objective optimization problems. This paper introduces a novel enhancement to the MOEA/D framework through the integration of a dynamic penalty function aimed at improving constraint handling. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments using the widely recognized CEC’2009 benchmark problems. Our methodology is rigorously compared against state-of-the-art multi-objective optimization algorithms, allowing for a comprehensive assessment of its performance. The experimental results show that our enhanced MOEA/D approach yields solutions that are not only competitive but, in specific instances, outperform those generated by the leading algorithms in the field. Additionally, we discuss the implications of our findings for future research and practical applications, highlighting the potential of our dynamic penalty function to advance the state of the art in constrained multi-objective evolutionary optimization.