Constrained optimization performance is assessed using two types of metrics. The first is a usual optimization performance metric, such as regret, cumulative regret or dominated hypervolume. The second concerns the fraction of total measurements made by a strategy that are infeasible. Both metrics are considered so as to not ignore the potential tradeoff between optimization performance and avoidance of infeasible measurements. Consider a SDL application where too many infeasible measurements result in a broken or disabled experimental setup necessitating a costly maintenance procedure to bring it back online, a researcher may elect to use a strategy which sacrifices some optimization performance for a reduced number of infeasible recommendations.