The numerical complexity of Discrete Element Method (DEM) simulations generally forces an idealisation of DEM models, making the calibration process the key to realistic simulation results. When calibrating cohesionless, free-flowing bulk materials, individual simple experiments are commonly used as reference for the calibration, such as the angle of repose in various test methods. Regardless of the experiment, the calibration is regularly performed by trial and error, systematic variation of the parameters, or using optimization algorithms until a suitable combination of parameters is found. This paper deals with the development and test of a highly efficient optimization-based calibration procedure. First, a brief overview of various optimization methods is given and explains why the use of surrogate models seems the best choice for DEM calibration. Subsequently, a new modular algorithm called "generalized surrogate modeling-based calibration" (GSMC) is presented. For testing the new algorithm, a modified draw down test is used. (C) 2019 Elsevier B.V. All rights reserved.