The number of planned lunar micro-rovers is increasing, yet few open, benchmarked tools exist to explore trade-offs in their design. Rover sizing is a highly coupled problem with a feedback loop between vehicle mass and mobility: a heavier rover sinks deeper into loose regolith, deeper sinkage increases power demand, more power requires a larger array, and the array adds mass. We present RoverDevKit, an open-source tradespace exploration toolkit with an evaluator that couples a closed-form wheel-soil model with bottom-up mass, solar-power, thermal-survival, and traverse models. The evaluator is fast enough to serve as the fitness function of a multi-objective genetic algorithm (NSGA-II): a search comprising 3,000 mission evaluations takes under a minute on a single laptop core. We use it to map trade-offs among distance traveled, vehicle mass, and slope capability on mare, polar, highland, and crater-rim missions. The limiting factor varies by mission: energy storage at high latitude, slope traction on loose highland regolith, and traverse range on mare and crater-rim terrain. The wheel-soil model reproduces measured single-wheel drawbar pull from two independent datasets with accuracy consistent with the 20-30% error typically reported for this class of model, and the mass model predicts published 5-50 kg rover masses with median absolute percentage error of 10.5%. A comparison with five published rover designs in the same mass class shows that they lie near the predicted Pareto front.

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