Multi-objective genetic algorithms were used to aerodynamically optimize a high-altitude long-endurance aircraft in order to improve its geometry and increase its performance. A conceptual design was created to satisfy a high-altitude long-endurance flight mission. However, to tackle the common problem high-altitude aircrafts have of generating a high CL with low speeds, the airfoil selected for the conceptual design was optimized to maximize its lift capacity at the cruise altitude. The optimization consisted on using adaptive range multi-objective genetic algorithms with 9 design variables represented as the PARSEC definition for an airfoil, and Xfoil was used as the evaluating tool for the generated airfoils. The optimization produced an airfoil that maximizes the required aerodynamic performance for high-altitude flight. This aerodynamic performance improvement eventually leads to the reduction of necessary wing area, which leads to weight and cost reductions which are significant for high-altitude unmanned aircrafts.