This study proposes an offline machine-learning (ML) algorithm that ranks candidate training points by scoring them based on their distance to the closest training point and on the local gradient of the surrogate estimate and then choosing the highest-rank point. The effectiveness of this method is confirmed in developing surrogates to solve a two-objective groundwater pumping optimization problem formulated on a three-dimensional island aquifer.