The process of generating reload configuration patterns is presented as a search procedure. The search space of the problem is found to contain ∼1012 possible problem states. If computational resources and execution time necessary to evaluate a single solution are taken into account, this problem may be described as a “large space search problem. ” Understanding of the structure of the search space, i.e., distribution of the optimal (or nearly optimal) solutions, is necessary to choose an appropriate search method and to utilize adequately domain heuristic knowledge. A worth function is developed based on two performance parameters: cycle length and power peaking factor. A series of numerical experiments was carried out; 300000 patterns were generated in 40 sessions. All these patterns were analyzed by simulating the power production cycle and by evaluating the two performance parameters. The worth function was calculated and plotted. Analysis of the worth function reveals quite a complicated search space structure. The fine structure shows an extremely large number of local peaks: about one peak per hundred configurations. The direct implication of this discovery is that within a search space of 1012 states, there are &sims;1010 local optima. Further consideration of the worth function shape shows that the distribution of the local optima forms a contour with much slower variations, where “better” or “worse” groups of patterns are spaced within a few thousand or tens of thousands of configurations, and finally very broad subregions of the whole space display variations of the worth function, where optimal regions include tens of thousands of patterns and are separated by hundreds of thousands and millions. The main conclusion is that the basic challenge of the reload configuration design is due to an extremely large search space and its complicated structure. Heuristically guided search seems to be well suited for this problem.