It is essential to further design built environments with improved thermal comfort level, air quality, and reduced energy consumption of the HVAC system. Recently, researchers integrated computational fluid dynamics (CFD) with optimization algorithms to design the desired built environments. The CFD-based genetic algorithm, which is developed by imitating the evolution theory, is able to identify the global optima. However, the method has high requirements for the computational resources since a single design would take numbers of CFD simulations. Therefore, it is necessary to accelerate the CFD-based genetic algorithm, which would extend its application in the inverse design of built environment. A direct way to accelerate the CFD-based genetic algorithm is to find a substitute for the CFD simulations. The integration of fast fluid dynamics (FFD) and genetic algorithm seems to be able to accelerate the inverse design without losing the accuracy. Therefore, this investigation developed a FFD-based genetic algorithm and implemented the model in OpenFOAM (Open Field Operation and Manipulation), which is an open source CFD program. This study compared the FFD-based genetic algorithm with CFD-based genetic algorithm on accuracy and efficiency in the inverse design of thermal comfort and air quality in an office.