Abstract: Most highway service areas(HSA) and electric vehicles(EV) in China consume traditional energy sources and have higher carbon emissions. To reduce the environmental pollution caused by electricity consumption of HSA and EV, this paper introduces a clean energy self-consistent rate and proposes an energy management and service strategy for HSA with integrated photovoltaic(PV)-storage-swapping. On the basis of guaranteeing swapping service, it aims to maximize economic efficiency and the clean energy self-reliance rate. This paper proposes an improved multi-objective quantum genetic algorithm for the optimization problem of energy management and service strategy in the HSA. Since the traditional quantum genetic algorithm suffers from premature convergence, poor flexibility and easy to fall into local optimum, this paper firstly introduces a microhabitat strategy in the population initialization process, secondly adaptively adjusts the crossover probability, variation probability and rotation angle and improves the quantum rotation gate, and then adopts an elite retention strategy to improve the convergence speed and increase the diversity of the population. Finally, the scheme is simulated and verified in an off-grid HSA micro-grid model with integrated PV-storage- swapping. The results show that the system achieves the double improvement of economic efficiency and clean energy self-consumption rate under the guarantee of battery swap demand, and achieves the purpose of carbon emission reduction.