Aircraft sizing studies consist in determining the main characteristics of an aircraft starting from a set of requirements. These studies can be summarized as global constrained optimization problems. The constraints express physical feasibility and the requirements to be satisfied; the objectives are market-driven performances of the aircraft. These optimizations are currently manually conducted as many input data frequently evolve during the study. This work introduced mathematical methods that are useful in a sizing tool to ease, fasten and enhance the aircraft configuration optimization problem. Using genetic algorithms, large amounts of design points satisfying the requirements were rapidly produced, despite some issues inherent to the aircraft model: numerical noise or physically meaningless design points due to the vast design space. Then, multicriteria optimization methods were introduced, as several criteria were considered concurrently. As calculation times became important, the aircraft model was substituted by a surrogate model. Radial basis functions approximated the constraint and the objective functions. Finally, a possible outcome of the integration of these different techniques was proposed in order to yield the engineers a global and operational perception of the design space.