The main objective of a bridge manager is to find the best maintenance plan for a group of bridges over a prescribed time horizon. The bridge manager usually faces conflicting objectives, as maintenance plans resulting in safer and less deteriorated structures also lead to higher costs. In general, the problem is posed as a deterministic single-objective optimization where cost is minimized keeping performance above predefined thresholds. However, single-objective optimization results in only one optimal solution that does not provide the advantages of considering other objectives and constraints. In addition, the effects of uncertainties are not taken into account or are included in a very simplified way. The bridge manager obtains, in this way, only one deterministic optimum maintenance plan, and not a set of different probabilistic maintenance solutions from which the best, for each particular situation, can be chosen. In this paper, a full probabilistic multiobjective approach to bridge maintenance considering single maintenance types is developed. This approach is based on the latest developments in bridge management by considering probabilistic continuous performance indicators and probabilistically defined objectives and constraints. The problem is solved using multiobjective genetic algorithms and a Latin hypercube sampling technique. Multiobjective applications to existing reinforced concrete bridge components under probabilistic deterioration and probabilistic-defined single maintenance types are presented and discussed.