The existing research of electric vehicle acceleration curves optimization mainly focuses on minimum energy consumption, without considering the battery life. This paper focuses on solving a multi-objective optimization problem with two conflicting objectives: minimization of energy consumption per kilometer and minimization of percentage of battery capacity loss per kilometer during acceleration process. The influence of the number and the variation trend of accelerations on these two objectives are simultaneously considered, and the acceleration curves are optimized using the fast elitist non dominated sorting genetic algorithm. The results obtained are selected by using the fuzzy theory. The results show that for the acceleration condition with zero initial velocity, the energy consumption per kilometer and the percentage of battery capacity loss per kilometer of multiple accelerations curves above the original acceleration curves all decreases. While for the high acceleration condition where initial velocity is not zero, the energy saving effect of the optimized multiple accelerations curves above the original condition is not obvious. Then, we analyze the reasons for energy consumption difference, and it is found that energy consumption per kilometer in overcoming accelerating resistance for optimization curves is much less than original condition for low velocity. It is also found that energy consumption per kilometer in accelerating resistance and aerodynamic resistance is large for high velocity, and there is little difference between optimized multiple accelerations curves and original condition. (C) 2018 Elsevier Ltd. All rights reserved.