As awareness of the potential for learned models to amplify existing societal biases increases, the field of ML fairness has developed mitigation techniques. A prevalent method applies constraints, including equality of performance, with respect to subgroups defined over the intersection of sensitive attributes such as race and gender. Enforcing such constraints when the subgroup populations are considerably skewed with respect to a target can lead to unintentional degradation in performance, without benefiting any individual subgroup, counter to the United Nations Sustainable Development goals of reducing inequalities and promoting growth. In order to avoid such performance degradation while ensuring equitable treatment to all groups, we propose Pareto-Efficient Fairness (PEF), which identifies the operating point on the Pareto curve of subgroup performances closest to the fairness hyperplane. Specifically, PEF finds a Pareto Optimal point which maximizes multiple subgroup accuracy measures. The algorithm scalarizes using the adaptive weighted metric norm by iteratively searching the Pareto region of all models enforcing the fairness constraint. PEF is backed by strong theoretical results on discoverability and provides domain practitioners finer control in navigating both convex and non-convex accuracyfairness trade-offs. Empirically, we show that PEF increases performance of all subgroups in skewed synthetic data and UCI datasets.