To address intensified load fluctuations, enlarged peak-valley differences and capacity stress caused by large-scale electric vehicle integration in residential scenarios, this paper studies the capacity-constrained optimization of aggregated orderly charging. A grouped multi-objective optimization model is established to coordinate charging cost, total load fluctuation and charging behavior deviation, while considering group-wise energy conservation, power limits, capacity constraints and scenario feasibility. Based on a decomposition-based multi-objective evolutionary framework and success-history based adaptive differential evolution, a structure-enhanced congestion-guided method is proposed. Energy-preserving pairwise perturbation, congestion-guided evaluation and local refinement are introduced to improve the search capability under complex constraints. Mild, medium and highly capacity-tight residential scenarios are constructed for validation. The results show that the proposed method achieves the best overall performance in all five formal experimental settings. In the highly capacity-tight scenario, compared with the main benchmark method, the solution-set approximation error is reduced by about 26.1%, and the solution-set coverage quality is significantly improved. The proposed method obtains an average rank of 1 and outperforms the benchmarks in all five scenarios, with an average runtime of 47.8 s. Ablation results indicate that energy-preserving pairwise perturbation is the key mechanism for improving solution quality, while congestion-guided evaluation and local refinement further enhance the performance under tight capacity constraints. The results verify the effectiveness of the proposed model and method in achieving safe charging-load transfer, load smoothing, and charging-behavior preservation under residential capacity constraints.