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The urban transit routing problem (UTRP) for public transport systems involves finding a set of efficient transit routes to meet customer demands. The UTRP is an NP-hard, highly constrained, multi-objective problem, for which the evaluation of candidate route sets can prove both time consuming and challenging, with many potential solutions rejected on the grounds of infeasibility. In this paper we propose a simple evolutionary multi-objective optimization technique to solve the UTRP. First we present a representation of the UTRP and introduce our two key objectives, which are to minimise both passenger costs and operator costs. Following this, we describe a simple multi-objective optimization algorithm for the UTRP then present experimental results obtained using the Mandl's benchmark data and a larger transport network.