This paper proposes a numerical method for the calibration of dynamic stochastic general equilibrium (DSGE) models. The method consists in the use of a hybrid algorithm, first to find the steady state, and then to minimize an objective function defined by the researcher according to the purposes of the calibration. The proposed method consists in the use of the Simulated Annealing algorithm followed by traditional optimization routines. The virtues of the algorithm are analyzed through Monte Carlo simulations, using a closed-economy model that has a steady state with no analytical solution. The results obtained in this exercise show that the proposed algorithm generates more precise results using less computational resources than traditional alternatives. Finally we present the calibration of a model for the Colombian economy, consisting of 179 equations and adjusted through 50 parameters to replicate 50 ratios. The maximum percent deviation of the steady-state ratios of the model with respect to the corresponding values in the Colombian economy is 7.9%, and in 29 of all the 50 cases this deviation is less than or equal to 1%.