With increasing rates of antibiotic resistance, bacterial infections have become more difficult to treat, elevating the importance of surveillance and prevention. Effective surveillance relies on the availability of rapid, cost-effective, and informative typing methods to monitor bacterial isolates. PCR-based typing assays are fast and inexpensive, but their utility is limited by the lack of targets which are capable of distinguishing between strains within a species. To identify highly informative PCR targets from the growing base of publicly available bacterial genome sequences, we developed pan-PCR. This computer algorithm uses existing genome sequences for isolates of a species of interest and identifies a set of genes whose patterns of presence or absence provide the best discrimination between strains in this species. A set of PCR primers targeting the identified genes is then designed, with each PCR product being of a different size to allow multiplexing. These target DNA regions and PCR primers can then be utilized to type bacterial isolates. To evaluate pan-PCR, we designed an assay for the emerging pathogen Acinetobacter baumannii. Taking as input a set of 29 previously sequenced genomes, pan-PCR identified 6 genetic loci whose presence or absence was capable of distinguishing all the input strains. This assay was applied to a set of patient isolates, and its discriminatory power was compared to that of multilocus sequence typing (MLST) and whole-genome optical maps. We found that the pan-PCR assay was capable of making clinically relevant distinctions between strains with identical MLST profiles and showed a discriminatory power similar to that of optical maps. Pan-PCR represents a tool capable of exploiting available genome sequence data to design highly discriminatory PCR assays. The ease of design and implementation makes this approach feasible for diagnostic facilities of all sizes.