The accurate interpretation of genetic variants is essential for clinical actionability. However, a majority of variants remain of uncertain significance. Multiplexed assays of variant effects (MAVEs), can help provide functional evidence for variants of uncertain significance (VUS) at the scale of entire genes. Although the systematic prioritization of genes for such assays has been of great interest from the clinical perspective, existing strategies have rarely emphasized this motivation. Here, we propose three objectives for quantifying the importance of genes each satisfying a specific clinical goal: (1) Movability scores to prioritize genes with the most VUS moving to non-VUS categories, (2) Correction scores to prioritize genes with the most pathogenic and/or benign variants that could be reclassified, and (3) Uncertainty scores to prioritize genes with VUS for which variant pathogenicity predictors used in clinical classification exhibit the greatest uncertainty. We demonstrate that existing approaches are sub-optimal when considering these explicit clinical objectives. We also propose a combined weighted score that optimizes the three objectives simultaneously and finds optimal weights to improve over existing approaches. Our strategy generally results in better performance than existing knowledge-driven and data-driven strategies and yields gene sets that are clinically relevant. Our work has implications for systematic efforts that aim to iterate between predictor development, experimentation and translation to the clinic.