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The conventional practice of retail electric utilities is to aggregate customers geographically. The utility purchases electricity for its customers via bulk transactions on the wholesale market, and it passes these costs along to its customers, the end consumers, through their rate plan. Typically, all residential consumers are offered the same per unit rate plan, which leads to cost sharing. Some consumers use their electricity at peak hours, when it is more expensive on the wholesale market, and others consume mostly at off peak hours, when it is cheaper, but they all enjoy the same per unit rate through their utility. This paper proposed a method for the utility to segment a population of consumers on the basis of their individual consumption patterns. An optimal recruitment algorithm was developed to aggregate consumers into groups with a relatively low per unit cost of electricity on the wholesale market. It was then proposed that the utility should group together enough consumers to ensure an adequately low forecast error, which is related to risks it faces in wholesale market transactions. Finally, it was shown that by repeated application of this process, the utility could segment the entire population into groups and offer them differentiated rate plans based on their actual consumption behavior. These groupings are stable in the sense that no one consumer can unilaterally improve her outcome.