Due to the rising pollution and greenhouse gas emissions resulting from fossil fuel-based transportation systems, researchers and policymakers are pushing for Electric Vehicle (EV) that is envisaged as an efficient, eco-friendly alternative. However, due to their limited range and battery capacity, EVs need frequent charging, which is time-consuming and available at specific locations. Therefore, proper charge scheduling and route management of EVs is essential and significant. This paper addresses this problem by proposing an intelligent heuristic mechanism that ensures that the EVs are always routed through a path that minimizes the energy consumption and the total time to travel. We formulate it as a multi-objective optimization problem considering real-world specifications and constraints and propose a graph-based multi-objective heuristic algorithm (MoHA) to obtain the desired solutions quickly. Further, multiple variants of the proposed algorithm are proposed, and comparative analysis is performed on practical datasets. The proposed algorithm is evaluated based on some of the well-known performance metrics for multi-objective approaches. The results obtained show that the energy-aware-MoHA variant produced 32.39% better results in minimizing energy consumption, and time-aware-MoHA performed better in optimizing average time requirements by 24.32%. Moreover, the initial ordering of the EVs has significant importance on the proposed algorithm's overall performance.

由于基于化石燃料的运输系统造成的污染和温室气体排放量不断增加，研究人员和政策制定者都在推动电动汽车（EV）的发展，电动汽车被认为是一种高效，环保的替代方案。然而，由于其范围和电池容量的限制，电动汽车需要频繁充电，这很耗时且可在特定位置使用。因此，正确的电动汽车充电计划和路线管理至关重要且意义重大。本文通过提出一种智能启发式机制来解决此问题，该机制可确保始终将电动汽车通过最小化能耗和总行驶时间的路径进行路由。我们将其公式化为考虑到现实世界中的规格和约束的多目标优化问题，并提出了一种基于图的多目标启发式算法（MoHA）以快速获得所需的解决方案。此外，提出了所提出算法的多种变体，并在实际数据集上进行了比较分析。基于一些用于多目标方法的著名性能指标，对提出的算法进行了评估。获得的结果表明具有能量意识的-MoHA变体在最小化能耗方面产生了32.39％的更好结果，而具有时间意识的-MoHA在优化平均时间需求方面表现更好，达到24.32％。此外，电动汽车的初始排序对所提出算法的整体性能具有重要意义。