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Enclosed parking garages require mechanical ventilation fans to dilute concentrations of polutants emitted from vehicles, which contributes to energy use and peak electricity demand. This study develops and applies a simulation framework combining multi-zone airflow and contaminant transport modeling, fan affinity laws, and realistic assumptions for vehicle traffic patterns and carbon monoxide (CO) emissions to improve our ability to predict the impacts of various ventilation control strategies on indoor air quality and fan energy use in parking garages. The simulation approach is validated using measured data from a parking garage case study and then applied to investigate fan energy use, peak power demand, and resulting CO concentrations for four different ventilation control strategies in a model underground parking garage under a variety of assumptions for model inputs. The four ventilation control strategies evaluated include one simplistic schedule (i.e., Always-On) and three demand-based strategies in which fan speed is a function of CO concentrations in the spaces, including Linear-Demand Control Ventilation (DCV), Standardized Variable Flow (SVF), and a simple On-Off strategy. The estimated annual average fan energy consumption was consistently lowest with the Linear-DCV strategy, resulting in average (± standard deviation) energy savings across all modeled scenarios of 84.3％±0.4％, 72.8％±3.6％, and 97.9％±0.1％ compared to SVF, On-Off, and Always-On strategies, respectively. The utility of the framework described herein is that it can be used to model energy and indoor air quality impacts of other parking garage configurations and control scenarios.