The calibration of hydrological models is here formulated as a Blackbox optimization problem where the only information available to the optimization algorithm is the objective function value. In the case of distributed hydrological models, the calibration process is often known to be hampered by computational efficiency issues. Running a single simulation may take several minutes, and the optimization process may require thousands of model simulations; the computational time can thus easily expand to several hours or days. A new hybrid optimization algorithm for the calibration of computationally-intensive hydrological models is introduced. It merges both the convergence analysis and robust local refinement from the Mesh Adaptive Direct Search (MADS) algorithm with the global exploration capabilities from the heuristic strategies used by the Dynamically Dimensioned Search (DDS) algorithm. The new hybrid method is applied to the calibration of the distributed and computationally-intensive HYDROTEL model on three different river basins located in the province of Québec (Canada). Results show that the hybrid DDS-MADS method can reduce the total number of required model simulations without compromising the quality of the final objective function value.