Against the backdrop of the “dual-carbon” goals and the rapid development of urban cold chain logistics, electric logistics vehicles have been widely deployed due to their advantages of low emissions and low operating costs. However, their operational performance is still constrained by limited driving range, uncertain charging behavior, and fluctuations in time-of-use electricity prices. Charging strategy selection plays a critical role in determining distribution cost and system efficiency. To address the charging decision-making problem of electric logistics vehicles under time-of-use electricity pricing, this paper develops an optimization model with the objective of minimizing total charging cost. The model incorporates practical operational constraints, including vehicle load capacity, state of charge safety thresholds, and customer time windows. A genetic algorithm is employed to solve the model, and case studies based on the Solomon dataset are conducted to compare distribution routes and charging costs under partial charging and full charging strategies. The results show that the partial charging strategy enables flexible “on-demand charging”, effectively reducing charging volume and charging time, thereby lowering total charging cost. In contrast, the full charging strategy improves driving range reliability and ensures delivery continuity. The findings provide decision support for logistics enterprises to select appropriate charging strategies under time-of-use electricity pricing.