A new variation of the user equilibrium-discrete network design problem (UE-DNDP) is proposed for achieving environmental justice (EJ) or equity among population groups. This research is motivated by the federal requirement that transportation plans using federal money include an evaluation of EJ and that the planning agency mitigate, where feasible, any disproportionate impacts on protected populations (i.e., minority and low-income groups). Eight potential objective functions focused on maximizing equity of congestion and travel time are developed and discussed with regard to their applicability for the upper level of this bilevel problem. On the basis of assumed knowledge of the origin-destination travel matrices by population group, numerical analysis is conducted to assess the performance of each proposed formulation. The lower-level UE problem is solved by using the Frank-Wolfe method, and because of the hard combinatorial nature of EJ-UE-DNDP, a selectorecombinative genetic algorithm is implemented to search the solution space for feasible network improvement strategies efficiently. The results of numerical analysis suggest that Pareto-optimal approaches can be successfully applied and that the most effective formulations minimize the difference between the change in congestion or travel time across population groups due to the selected improvement projects.