Software product line testing is challenging. The similarity-based testing method can improve testing coverage and fault detection rate by increasing the diversity of test suites. Due to its excellent scalability and satisfactory testing effects, the method has become one of the most important test methods for software product lines. How to generate diverse test cases and how to maintain the diversity of test suites are two key issues in this test method. To handle the above issues, this study proposes a software product line test algorithm based on diverse SAT solvers and novelty search (NS). Specifically, the algorithm simultaneously uses two types of diverse SAT solvers to generate diverse test cases. In particular, in order to improve the diversity of stochastic local search SAT solvers, the study proposes a general strategy that is based on a probability vector to generate candidate solutions. Furthermore, two archiving strategies inspired by the idea of the NS algorithm are designed and applied to maintain both the global and local diversity of the test suites. Ablation and comparison experiments on 50 real software product lines verify the effectiveness of both the diverse SAT solvers and the two archiving strategies, as well as the superiority of the proposed algorithm over other state-of-the-art algorithms.