Estimating car traffic is crucial in many big cities around the world to provide users with good alternatives for their travels but also to help decision makers while evaluating the impact of road pricing, new roads, or short-term road works. It will, in the best case, reduce traffic jams drastically resulting in a significantly smaller production of atmospheric greenhouse gases. The estimation of traffic relies thereby on a precise monitoring of the current traffic as well as on a simulation which can reproduce the observed data reliably. To match the observed traffic and the simulation outputs, an optimization of parameters of both the model and the simulator itself is necessary. Within this paper, we present a first study of how state-of-the-art evolutionary computation approaches can be employed in such a scenario. In particular, we use the well-known Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to calibrate the dynamic car traffic simulator Metropolis. As the calculation of the objective function is expensive, a parallel evaluation of the fitness is implemented. First experiments for a simplified city network of Sioux Falls, SD, USA show that the approach is working in principle but also that the objective function contains noise. An easy way to deal with noise within CMA-ES is to simply increase the population size|experiments on easy-to-calculate noisy test functions support this impact exemplary. Additional traffic calibration runs with larger population size, however, do not support this impact but make us believe that numerical precision problems within the simulator are the reason for the noise observed.