Optimization tools are used to recommend low-emission engine combustion chamber designs, spray targeting, and swirl ratio levels for a heavy-duty diesel engine operated at low load and high load. A non-dominated sorting genetic algorithm (NSGA II) was coupled with the KIVA computational fluid dynamics (CFD) code, as well as with an automated grid generation technique to conduct the multi-objective optimizations with goals of low emissions and improved fuel economy. The study identifies the aspects of the combustion and pollution formation that are affected by mixing, and offers guidance for better matching of the piston geometry with the spray plume geometry for enhanced mixing. By comparing the optimal results of low-load and high-load cases, the study reveals that different injection strategies and matching of the piston geometry with the spray plume are needed for different operating conditions. A non-parametric regression analysis tool was also used to post-process the optimized results in order to provide an understanding of the effects of each optimized parameter on fuel economy and pollutant formation. It was found that an optimal combination of spray targeting, swirl ratio, and bowl geometry exists that simultaneously minimizes emissions formation and offers improved fuel consumption.