In the past decade, the number of battery electric vehicles (BEVs) on the road has been growing rapidly in response to global climate change and cyclic gasoline shortages. Due to the limited driving range of most commercial BEVs, individuals who use BEVs for long-distance travel tend to spend much more time on the road than owners of traditional internal combustion engine vehicles. To reduce travel time in long-distance trips, a social-aware trip planner is necessary to coordinate driving speed, vehicle charging, and social activities (e.g., dining, visit of places of interest). This paper formulates this travel time minimization problem into a mixed-integer programming model and utilizes genetic algorithm (GA) to solve for the optimal driving speed, vehicle charging, and the schedule of dining. The proposed planner is first tested numerically based on two real-world routes. Then, Monte Carlo simulations are performed to give a thorough analysis on the performance of the proposed planner. The simulation results show that the proposed method outperforms the baseline on both routes. Additionally, real-world tests are conducted to further validate the accuracy of the mixed-integer programming model.