The current state-of-the-art design optimization of airframes is tightly wounded to its loads analyses as the process is usually conducted employing a deterministic set of critical load cases. The sheer number of scenarios required to estimate the critical loading conditions prevent these two processes from integrating. In this thesis, we address the problem of an efficient estimation of critical dynamic aeroelastic loads. The method is based on the Kriging metamodeling technique and the Expected Improvement Function, known formally as the Efficient Global Optimization (EGO) algorithm. Furthermore, different inexpensive metrics, based on the concept of Modal Contribution Factors, are investigated as indicators to determine if a substantial change in the loads has occurred during the design optimization, triggering the re-exploration of the design space. A case study is presented to evaluate the performance of the proposed methodology, where a reduction of 84 percent in the total time of execution was achieved.