Water, an essential resource for diverse purposes like environmental protection, urban water supply, energy generation, and agriculture, faces intensifying demand amid depleting supplies. Multi-Objective Optimization (MOO)-models are vital for addressing complex water system challenges with limited resources. However, varying approaches to distributive justice in these models introduce normative bias, leading to uncertainty in the derived implications from the model. This thesis is the first approach to understanding how the operationalization of distributive justice shapes the implications drawn from the 'optimal' outcomes of MOO-models. This thesis studied: 'How do different operationalization formulations for inequality in existing multi-objective optimization models shift the Pareto front?'. A rival framings approach acknowledges diversity in perspectives, for which it is suitable to contrast the operationalization formulation. The rival framing focused on the inequality metric and the aggregation method over time for this metric, both used for the formulation of inequality in the objective formulation. The case study revolves around the Conowingo Reservoir in the Lower Susquehanna River Basin. Utilizing an Evolutionary Multi-Object Direct Policy Search (EMODPS)-model, the research optimizes water allocation by incorporating an equality objective alongside baseline efficiency goals. Results underscore that the trade-off between equality and efficiency is highly dependent on the chosen operationalization for equality. Moreover, for higher levels of equality, the Pareto front will shift drastically in terms of strength and direction of trade-offs. By unravelling the complexities of justice's integration into MOO-models, advances are made in the comprehension of how distributive justice can inform decision-making. Through the elaboration of justice formulations, a future is reachable where justice is not only considered but reached.