To provide feasible primer sets for performing a polymerase chain reaction (PCR) experiment, many primer design methods have been proposed. However, the majority of these methods require a relatively long time to obtain an optimal solution since large quantities of template DNA need to be analyzed. Furthermore, the designed primer sets usually do not provide a specific PCR product size. In recent years, evolutionary computation has been applied to PCR primer design and yielded promising results. In this article, a memetic algorithm (MA) is proposed to solve primer design problems associated with providing a specific product size for PCR experiments. The MA is compared with a genetic algorithm (GA) using an accuracy formula to estimate the quality of the primer design and test the running time. Overall, 50 accession nucleotide sequences were sampled for the comparison of the accuracy of the GA and MA for primer design. Five hundred runs of the GA and MA primer design were performed with PCR product lengths of 150-300 bps and 500-800 bps, and two different methods of calculating T-m for each accession nucleotide sequence were tested. A comparison of the accuracy results for the GA and MA primer design showed that the MA primer design yielded better results than the GA primer design. The results further indicate that the proposed method finds optimal or near-optimal primer sets and effective PCR products in a dry dock experiment. Related materials are available online at http://bio.kuas.edu.tw/ma-pd/. (C) 2009 American Institute of Chemical Engineers Biotechnol. Prog., 25: 745-753, 2009