In this paper, we describe a new multiple change point detection technique based on segmenting the time series under study into subsequences. These segments correspond to the episodes that are likely to contain a unique jump. They are found by applying Bayesian decision theory through the minimization of simple cost functions. All calculations can be performed explicitly, without falling back on Markov chain Monte Carlo methods and resulting in particularly light implementation. Through prior distributions derived from a stochastic renewal process description of jump occurrences, expert knowledge of jump amplitude and return period is also introduced in our decision process. Comparison to several multiple change point methods on simulated series lead to similar or better performance, achieved at lower computational cost.