The present work proposes a methodology for diesel engine development using multi-dimensional CFD and computer optimization. A multi-objective genetic algorithm coupled with the KIVA3V Release 2 code was used to optimize a high speed direct injection (HSDI) diesel engine for passenger car applications. The simulations were conducted using high-throughput computing with the CONDOR system. An automated grid generator was used for efficient mesh generation with 11 variable piston bowl geometry parameters. The first step in the procedure was to search for an optimal nozzle and piston bowl design. In this case, spray targeting, swirl ratio, and piston bowl shape were optimized separately for two full-load cases using simpler efficient combustion models (the characteristic time scale model and the shell ignition model). The optimal designs from the two optimizations were then validated using a combustion model with detailed chemistry (KIVA-CHEMKIN model and ERC n-heptane mechanism). Next, the best design was selected, which simultaneously reduces fuel consumption and pollutant emission for the two cases. Its nozzle design and piston bowl shape were fixed and real-time controllable parameters were optimized for each representative operating condition, including full-load, mid-load, and low-load operation. The controllable parameters, SOI, swirl ratio, boost pressure, and injection pressure, were optimized using a recently developed adaptive multi-grid chemistry (AMC) model that allows for efficient simulations with the detailed chemistry combustion model. Optimal designs which simultaneously reduce fuel consumption and pollutant emissions were obtained for all the operating conditions except a very low-load case. Averaged optimal boost pressure and injection pressure were found to increase with the engine load.