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The determination of optimal Surveillance Test Intervals (STI) is a matter of great importance in risk-informed applications, due both to the implications that maintenance actions have on the safety of the risky plants such as the nuclear ones, and to the important amount of resources invested in maintenance operations by the industrial organizations. The common approach to determining the optimal STI uses a simplified system-availability model relying on a set of parameters at the component level (failure rate, repair rate, frequency of failure on demand, human error rate, inspection duration, etc.), whose values are typically estimated on the basis of few, sparse data, and can suffer from appreciable uncertainties. Thus, the prediction of the system behavior on the basis of the parameters' best estimates is scarcely significant, if not accompanied by some measure of the associated uncertainty, such as the variance. This paper proposes a multi-objective optimization approach, based on genetic algorithms, which transparently and explicitly account for the uncertainties in the parameters. The objectives considered (the inverse of the s-expected system failure probability and the inverse of its variance), are such as to drive the genetic search toward solutions which are guaranteed to give optimal performance with high assurance. For validation purposes, a simple case study regarding the optimization of the layout of a pipeline is firstly presented. The procedure is then applied to a more complex system taken from literature, the Residual Heat Removal safety system of a Boiling Water Reactor, for determining the optimal STI of the system components. The approach provides the decision maker with a useful tool for determining those solutions which, besides being optimal with respect to the s-expected safety behavior, allow a high degree of assurance in the actual system performance.