A testability test optimization method based on subsystem contribution rate and prior distribution credibility was proposed to deal with the problems such as the difficulty in obtaining system-level prior information, the unreasonable calculation of prior distribution weight, and the large sample size in existing methods. First, the testability multi-source prior information was systematically analyzed and the subsystem contribution rate was defined. On this basis, the subsystem prior data was converted to obtain the system-level prior data by the information theory. Then, the similarity measure of prior distribution was introduced to characterize the compatibility between prior distribution and experimental data. Next, the technique for order preference by similarity to ideal solution-analytic hierarchy process (TOPSIS-AHP) method was proposed to determine the credibility of prior distribution, and then the mixed prior distribution was obtained. Finally, on the basis of the mixed prior distribution determined by subsystem prior information, the sequential posterior odd test (SPOT) method was used to make the test optimization plan. Case analysis results show that the mixed prior distribution obtained from the data conversion method based on contribution rate and the weight calculation method based on credibility was more accurate. The sample size of the SPOT method decreased by 18.6% on average compared with the sequential probability ratio test (SPRT) method, and was 61.1% smaller than the Bayes method. Besides, this method could effectively reduce the risk of both sides. The SPOT method considering contribution rate and credibility has good application effects in acquisition of prior information, weight of prior distribution, number of test samples, and risk of both sides.