With increasingly complex constrained multi-objective problems in real-world scenarios, existing enhanced differential evolution algorithms often struggle to balance global and local searches, leading to entrapment in local optima. To address this limitation, this paper proposes a constrained multi-objective differential evolution framework integrating phase-wise hierarchical organization and adaptive ε-method. The framework combines a differential evolution algorithm featuring stage stratification and dual-balanced mutation strategies with constrained multi-objective optimization techniques. Leveraging the inherent convergence capability and population diversity of the enhanced differential evolution algorithm, this integrated approach generates offspring with improved evolutionary potential. The incorporation of enhanced differential evolution effectively addresses the equilibrium between global exploration and local exploitation. The proposed framework has been tested on MW and LIRCMOP benchmarks. The experimental results demonstrate that the performance of proposed method outperforms other state-of-the-art methods.