Changes in station instrumentation, location, or observer can often induce artificial discontinuities into climatic time series. For example, United States temperature recording stations average about six station relocation and instrumentation changes over a century of operation. Many of these changepoint times are documented in station histories; however, other changepoint times are unknown for a variety of reasons. Even when a changepoint time is known, one may still question whether the change instills a mean shift in series observations. This talk introduces an information based approach to the multiple changepoint identification (segmentation) problem. Our methods are specifically tailored to climatic time series in that they allow for periodicities and autocorrelations. The objective function gauging the number of changepoints and their locations, based on a minimum description length (MDL) information criterion, is derived. A genetic algorithm is then developed to optimize the objective function. The methods are applied in the analysis of a century of monthly temperatures from Tuscaloosa, Alabama.