Crystallization is a widely used unit operation for the production of pharmaceuticals, fertilizers, and fine chemicals. A commonly used crystallization operational objective is to produce large crystals under minimum nucleation rates. For cooling crystallization there are two key methods for minimizing nucleation, programmed cooling and seeding. In this paper, we evaluate the cooling and seeding methods through the detailed modeling of nucleation phenomena coupled with a population balance and dynamic optimization of this mathematical formulation. Extensive simulation results showed that initial seeding parameters, as proclaimed by others, do have a significant effect on the final product crystal size distribution. It also showed the significance of a combined seeding-cooling approach where a joint cooling and seeding optimization gives superior performance to just optimizing the seed. Importantly, the developed model was highly instrumental in rapidly determining optimal combined seeding-cooling profiles via dynamic model-based optimizations. (C) 2010 Elsevier Ltd. All rights reserved.