Evolutionary methods can be used to infer the time scale of groups of organisms. In some pathogens it is possible to estimate the time of origin of infectious outbreaks or of disease emergence. The accuracy of these estimates, however, relies on our understanding of the rate at which genetic change accumulates over time. The recent surge of genomic data presents an unprecedented opportunity to improve our understanding of pathogen evolution, with the potential of improving future inferences of their evolutionary time scale. We estimated the rate of evolution for 36 complete genomes of different bacterial pathogens using a range of computational methods. We find large differences in the rates. For example, some bacteria, such as those that causes hospital-derived infections, evolve much faster than those that cause tuberculosis, which undergo extended periods of latency. We investigated characteristics of the bacteria that may explain variation in their rates. We find that genome size, genome composition, and the sampling time appear to play an important role in determining their rate. Our results provide the first genomic perspective of bacterial rates of evolution, thereby improving our understanding of the time scale over which they evolve.