Finding the energy-optimal route in the context of parcel delivery with electric vehicles (EVs) is more complicated than for conventional internal combustion engine (ICE) vehicles, where the energy cost of a path is mostly determined by the total traveled distance. In the case of EV delivery, the total energy consumption strongly depends on the order of delivery because the efficiency of the EV is affected by how the transported weight changes over time as it directly affects the battery efficiency. This makes impossible to find an optimal solution using traditional routing algorithms such as the traveling salesman problem (TSP) using a static quantity (e.g., distance) as a metric.In this paper, we propose a solution for the least-energy delivery problem using EVs; we implement an electric truck simulator and evaluate different static metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties, and is sensibly faster than state-of-the-art TSP heuristic algorithms.