In line with the proposing process of the self-adaptive discrete differential evolution (SaDDE) algorithm, this research focuses on the strategy selection problem. The strategy pool plays a significant role in the SaDDE algorithm, and there are three issues need to be addressed in designing the strategy pool: (1) how to determine if a candidate solution generating strategy (CSGS) is effective; (2) which CSGSes to choose to constitute the strategy pool; and (3) how to find a suitable size forthe strategy pool. In order to solve these problems, a relative permutation order based scale method (RPOSM) and a RPOSM based analytic hierarchy process (RPOSM-AHP) are proposed in this paper. The experiments are mainly conducted on six test instances (T_INSes) which come from an electronic countermeasure (ECM) simulation experimental platform. 144 different CSGSes are designed, and 144×6 independent experiments are performed to obtain the sort sequences of the CSGSes. The RPOSM and the RPOSM-AHP are adopted to obtain the priority vector of the 144 CSGSes. Sequentially, 16 algorithms with different sizes of strategy pools are constructed and their performance is tested on the six T_INSes. Further, the RPOSM and RPOSM-AHP are employed again to find the suitable pool size for the SaDDE algorithm. Computational comparisons demonstrate that, within fixed number of fitness evaluations (NFE), the SaDDE algorithm can generate better results than its competitors.