In this study, we contribute a comprehensive framework for simultaneously assessing solution quality and scalability for massively parallel multiobjective evolutionary algorithm (MOEA)-based search using a highly challenging optimization-assimilation application. Visual analytics are used to evaluate how changes in search metric performance relate to actual decision relevant changes in the Pareto approximate set. The application focuses on a four objective groundwater monitoring application in which parallel scalability is tested across compute core counts ranging from 64 to a maximum of 8192. This study demonstrates that parallel search performance must be assessed in terms of how well speedup is exploited to improve the quality of search results and that solely focusing on differences in computational time can be deceptive. Our results demonstrate how visualization can clarify when an MOEA's search shifts from "translating" the approximation set to "diversifying" its coverage over the extent of the objectives. This is an important observation. If shorter parallel run durations are required, the rapid early translation of the set may yield a reasonable approximation of the Pareto approximate set where further search is unnecessary. Although a groundwater application is used to demonstrate our parallelization, the visual analytics and metrics utilized to characterize the parallel scalability of MOEA-based search are broadly applicable in water resources and beyond.