Revenue management (RM) theory and practice frequently rely on simulation modeling. Simulations are employed to evaluate new methods and algorithms, to support decisions under uncertainty and complexity, and to train RM analysts. To be useful in practice, simulations have to be validated. To enable this, they are calibrated: model parameters are adjusted to create empirically valid results. This paper presents two novel approaches, in which genetic algorithms (GA) contribute to calibrating RM simulations. The GA emulate analyst influences and iteratively adjust demand parameters. In the first case, GA directly model analysts, setting influences and learning from the resulting performance. In the second case, a GA adjusts demand input parameters, aiming for the best fit between emergent simulation results and empirical revenue management indicators. We present promising numerical results for both approaches. In discussing these results, we also take a broader view on calibrating agent-based simulations.