We have developed an approach to acquire complicated user optimization criteria and use them to guide iterative solution improvement. The effectiveness of the approach was tested on job shop scheduling problems. The ill-structuredness of the domain and the desired optimization objectives in real-life problems, such as factory scheduling, makes the problems difficult to formalize and costly to solve. Current optimization technology requires explicit global optimization criteria in order to control its search for the optimal solution. But often, a user’s optimization preferences are state-dependent and cannot be expressed in terms of a single global optimization criterion. In our approach, the optimization preferences are represented implicitly and extensionally in a case base. Experimental results in job shop scheduling problems support the hypotheses that our approach (1) is capable of capturing diverse user optimization preferences and re-using them to guide solution quality improvement, (2) is robust in the sense that it improves solution quality independent of the method of initial solution generation, and (3) produces high quality solutions, which are comparable with solutions generated by traditional iterative optimization techniques, such as simulated annealing, at much lower computational cost.