Dynamic Traffic Assignment (DTA) models are widely used in transportation system management. Calibration is a crucial step to improve the reliability and the accuracy of DTA models. We present a systematic framework to offline calibrate the supply and demand component of a DTA model. The essence of model calibration is an optimization problem, aiming to minimize the discrepancy between field conditions and simulated traffic measurements. To overcome limitations of a single optimization algorithm, a joint approach is developed for the calibration of supply and demand component respectively with different traffic measurements. As the calibration process is a nonlinear and stochastic problem, heuristic algorithms: the Genetic Algorithm (GA) and the Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm, are implemented as a complement solution. Instead of using the standard GA, to expedite searching efficiency, we introduce the Islands Genetic Algorithm (IGA) and SPSA with Principal Component Analysis (PC-SPSA) to solve the calibration problem. A case study on a network of Munich, Germany, is used to validate the proposed methodology. The promising results indicate that calibration of the supply and demand component of a DTA model with the proposed joint approach improves modelling accuracy. In comparison, IGA outperforms standard GA in terms of convergence speed and solution quality.