为了提高城市配送车辆装载效率，降低配送过程碳排放量，构建了三维装箱约束下的燃油消耗车辆路径问题的数学模型，提出了一种自适应Memetic NSGA-II耦合求解算法。首先，将基于NSGA-II的非支配排序和拥挤距离机制作为全局搜索框架，融合了满足后进先出约束的三维装箱模块与可变邻域搜索、大规模邻域搜索等局部优化策略。同时采用分级停滞响应机制，根据进化状态自适应地激活多种高级重构策略，以维持种群多样性并有效跳出局部最优。基于改造后的3L-CVRP标准算例，求得一组在碳排放与装载率间权衡的Pareto最优解集。消融实验证明，移除核心组件均会导致算法性能显著下降。为验证该算法的可行性与优越性，将其与超启发式蚁群算法和三维自适应大邻域搜索等先进算法进行对比分析，算法在多数算例上表现更优，尤其在大规模算例中性能提升超过20%，可实现该问题的高质量求解。

To improve the loading efficiency of urban delivery vehicles and reduce carbon emissions during the distribution process, this paper establishes a mathematical model for the Fuel Consumption Vehicle Routing Problem with Three-dimensional Loading constraints and proposes an adaptive Memetic Non-dominated Sorting Genetic Algorithm II (Memetic NSGA-II) for its solution. Firstly, the algorithm utilizes the non-dominated sorting and crowding distance mechanisms of NSGA-II as its global search framework, integrating a three-dimensional packing module that satisfies the Last-In-First-Out (LIFO) constraint with local optimization strategies such as Variable Neighborhood Search (VNS) and Large Neighborhood Search (LNS). Furthermore, a hierarchical stagnation response mechanism is adopted, which adaptively activates various advanced reconstruction strategies based on the evolutionary state to maintain population diversity and effectively escape from local optima. Based on adapted 3L-CVRP benchmark instances, a set of Pareto optimal solutions representing the trade-off between carbon emissions and loading rates is obtained. Ablation studies demonstrate that removing the core components leads to a significant degradation in algorithm performance. To validate its feasibility and superiority, the proposed algorithm is benchmarked against state-of-the-art algorithms such as Hyper-heuristic Ant Colony Optimization (HHACO) and Three-Dimensional Adaptive Large Neighborhood Search (3D-ALNS-H). The results show that the proposed algorithm performs better on most instances, achieving a performance improvement of over 20% on large-scale instances and enabling high-quality solutions for this class of problem.