Pavements heavily influence the management costs in highway networks. Operating pavements represents a challenging task involving complex decisions on the application of maintenance actions to keep them at a reasonable level of performance. The major difficulty in applying computational tools to support decision making lies in a large number of pavement sections as a result of a high length of road networks. This paper addresses maintenance scheduling for pavements by consolidating this task into two stages. In each stage, multiobjective optimization is used to optimize maintenance schedules. The main motivation is to obtain a computationally treatable model for large road networks. The first stage is defined by a collection of pavement sections composing the road network. In this stage, the performance and maintenance models are addressed. These models account for uncertainties in the future performance and effects of maintenance by defining model parameters as random variables. The second stage refers to combining maintenance schedules for individual sections to determine the optimal maintenance plan at the network level. The results obtained for the real road network demonstrate the validity and usefulness of the proposed framework. Moreover, this framework is general and can be extended to different types of infrastructure assets.