Prostate-specific antigen (PSA) test is a commonly used clinical examination to evaluate the risk of prostate cancer, with the antibodies used normally as the recognition molecules for measuring PSA levels in serum. Alternatively, aptamers that are able to bind target molecules with high affinity and specificity similar to antibodies could be generated much easier and cheaper than the production of antibodies. In this study, we used computaional and experimental approaches to select truncated PSA-binding aptamers generated from the sequence information of PSA-binding aptamers previously reported in a literature. Genetic algorithm, the analysis of secondary structure, and molecular simulation were utilized in the in silico analysis. The top 4 ranked sequecnes in silico analysis were evaluated through their PSA-binding ability on the quartz crystal microbalance (QCM) biosensor. Finally, We identified a truncated aptamer obtained from the selection showing a nearly 3.5-fold higher measured signal than the response produced by the best known DNA sequence in the QCM measurement.