Empirical and mechanistic experimental design methods are combined to construct partial models, which are, thus, used to design a process. The grid algorithm restricts the next experimental point to potential process optima, according to the confidence intervals around the optimal points, and works with any experimental design algorithm such as D-optimal. Two case studies show the advantages of implementing the grid algorithm. On average the improvement due to the grid algorithm was 15-20% in the first case study. The second case study is based oil thin film growth using four potential models, with the most probable model used for experimental design. The grid algorithm balances the trade-off between two extremes: D-optimal designs and sampling at the predicted optimal point. The methodology presented shows that the experimenter does not have to decide ahead of time on purely empirical or mechanistic experimental design methods, since both may be useful. (c) 2008 American Institute of Chemical Engineers AIChE J, 55: 342-353, 2009