To enhance the aerodynamic performance of a solar-powered unmanned aerial vehicle (UAV), this study investigated its geometric shape optimization. First, according to the aerodynamic performance requirements of tailless configurations, the overall aircraft layout scheme was designed and parametrically modeled. Second, software modules including CATIA geometric modeling, SpaceClaim preprocessing and Fluent discrete solution were integrated via the Isight platform to establish a fully automatic simulation framework covering the whole workflow of ″modeling-preprocessing-numerical calculation″. Combined with the Kriging surrogate model and non-dominated sorting genetic algorithm (NSGA-Ⅱ), an efficient aerodynamic shape optimization system was constructed. Finally, multi-objective and multi-constraint aerodynamic shape optimization was carried out under the low-Reynolds-number cruise condition of the UAV, where the minimum drag coefficient and minimum pitching moment coefficient were taken as optimization objectives, while the lift-drag ratio and projected area were set as constraint conditions. The results show that the optimized UAV features increased wing-sweep angle and aspect ratio. The scope of low-pressure region on the upper wing surface and the pressure difference between upper and lower wing surfaces both rise accordingly, delaying flow separation at the trailing edge and reducing drag. At the design angle of attack, the lift coefficient increases by 3.9%, the drag coefficient decreases by 5.5%, and the pitching moment coefficient is reduced by 16.8%. At off-design angles of attack, the lift-to-drag ratio also improves. Overall, the aerodynamic efficiency and flight stability of the UAV are significantly improved. The proposed optimization framework provides a valuable technical reference for enhancing the aerodynamic performance of UAVs.