Abstract. In this paper we present a two-level approach that combines an adjoint-based gradient search method with an evolutionary algorithm for optimal active flow control. The suggested method effectively combines the advantages of both approaches and achieves a good compromise between the computational effort and the degree of freedom used in optimization. In the first level, a global optimization is performed with few design parameters using an evolutionary algorithm. In the second level, the global optimal solution from the first level is taken as the initial setting for the adjoint based local optimization using a large number of design parameters. The unsteady discrete adjoint solver required for the second level is developed based on Algorithmic Differentiation techniques for the unsteady incompressible flowsgoverned by Unsteady Reynolds-Averaged Navier Stokes (URANS) equations. In this way, the discrete adjoint solver is robust and has exactly the same functionality with the underlying URANS flow solver. The applicability of the two-level method is demonstrated by finding the optimal parameters of the active flow control mechanism on a three element airfoil configuration at a Reynolds number of Re = 10 6 and an angle of attack of AoA = 6 • . Numerical results have shown that the hybrid approach completely suppressed the separation and very significantly increased the mean-lift coefficient by 67% compared to the un-actuated baseline flow.