Since experimentation is expensive with increasing computation power the significance of modeling, simulation, and optimization in process development has grown. Quite often in such models some parameters are uncertain, e.g., due to high variance in experimental data used for their estimation. Methods for investigating the impact of uncertain model parameters in the simulation and a new extension of an adaptive multi-criteria optimization method to account for these uncertainties are described and demonstrated based on a cumene process.