Nanocrystalline aluminum offers a unique combination of mechanical properties such as high tensile strength, extended fatigue life, and wear resistance. Because of their enhanced properties, these materials have potential for applications in critical lightweight structural components. However, the small grain size of these metals leads to thermodynamic instability against grain coarsening and phase precipitation. To this end, this work focuses on identifying metallic dopants, which segregate to the grain boundaries in aluminum, that increase its stability against grain coarsening and phase precipitation. Since the design space, comprised of nanocrystalline aluminum-dopant combinations at unique compositions, is sufficiently large, we propose a multi-objective optimization of material properties and thermodynamic stability based on Non-dominated Sorting Genetic Algorithms to synthesize and evaluate Pareto optimal nanocrystalline aluminum-dopant designs.