Tens of thousands of single nucleotide polymorphisms (SNPs) are currently available for genome-wide association study (GWAS). Detecting disease-associated SNP-SNP interactions is considered an important way to capture the underlying genetic causes of complex diseases. In the combinatorially explosive search space, evolutionary algorithms are promising in solving this difficult problem because of their controllable time complexity. However, in existing evolutionary algorithms, some possible SNP-SNP interactions are evaluated multiple times by the fitness function. Such reevaluations not only waste computing resources but also make these algorithms easy to fall into local optima. To tackle this drawback, a progressive screening memetic algorithm (PSMA) is proposed in the paper. PSMA first represents all possible SNP-SNP interactions in a constructed graph. The proposed algorithm uses the progressive screening strategy to guarantee that every possible SNP-SNP interaction can only be evaluated once by reducing the constructed graph. Furthermore, two types of local search algorithms are introduced to enhance the detecting power of PSMA. Experimental results show that our proposed method outperforms other existing state-of-the-art methods for detecting disease-associated SNP-SNP interactions.