Child booster seats vary in size, shape and material, and the effects of these parameters on occupant response are not well understood. Large-scale computational studies are a means to explore the effects of these parameters across the space of potential booster designs. This work lays the foundation for such large-scale studies by quantifying the current booster design space, implementing a booster parameterization methodology, and comparing results of simplified, parametric booster models with similar, existing finite element models. These parameterized boosters are integrated into a simulation pipeline that positions and settles a child human body model, routes the seatbelt, and runs a frontal impact pulse – all automated to allow user-free execution of hundreds of simulations spanning the booster design space. Results include measurements of a 44 booster test sample, which was constructed from a set of 20 booster seat parameters across the categories of booster geometry, construction, lap belt routing, shoulder belt routing, and subject posture. Simplified booster seats generated with the parameterization methodology demonstrated similar occupant response when compared to more complex finite element booster models. The automation pipeline proved to be a robust tool, allowing much larger studies to be undertaken than would be possible with a manual approach.