Traditional optimal linear design problems are formulated by linear programming with a single criterion (objective)and a single resource availability (right-hand side) level. This approach is to seek an optimal solution for a given design system. However, it fails to deal with optimal linear design problems with multi-criteria and multi-source availability levels. In this paper, we first sketch how to use the multi-criteria and multi-constraint levels (MC2) linear programming to formulate the optimal linear design problems and to find a set of potentially good designs (PGDs). Then we generate generalized good designs (GGDs) from given PGDs and show how to construct their related dual contingency plans under various decision situations, fn contrast to the known concept of PGDs, the GGDs go beyond the limitations of the design opportunities. In constructing the dual contingency plans for a given GGD, we adjust the unit contribution of selected design opportunities in the GGD to convert non-optimal solutions into optimal solutions. Based on theoretical results, an algorithm of effectively and systematically locating the optimal generalized designs and the corresponding optimal dual contingency plans under various decision situations is provided.