In this paper, the performance of Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA), which has been developed for reducing a number of function evaluations, is examined by using three analytical test problems. These test problems are also solved by a widely-used Multi-Objective Evolutionary Algorithm (MOEA), NSGA2, and two gradient-based methods, Sequential Quadratic Programming (SQP) and Dynamic Hill Climber (DHC) for comparison. ARMOGA is found to locate a Pareto front with a small number of function evaluations comparable to DHC. To utilize the present ARMOGA, an automated design system of low-boom Supersonic Transport (SST) configuration has been developed. To reduce the sonic boom for supersonic flight effectively with minimizing the drag, SST wing-fuselage configurations equipped with a canard are considered. The resulting system automatically generates unstructured grids around SST canard-wing-fuselage configuration.