Optimal sensor placement is one that maximizes the likelihood of identifying future damage models. Based on assumptions from engineers, damage models of a structure are simulated and their predictions are computed. Computational approaches are used to place sensors at locations that maximize the chances of identifying damage. This paper studies the application of global search for optimal sensor placement. The global search methodology uses stochastic sampling to find optimal locations for sensors. In a previous study, Robert-Nicoud et al. proposed a greedy strategy that places sensors sequentially at locations where model predictions have maximum entropy. Performance of the two strategies are compared for the Schwandbach bridge in Switzerland. The results show that global search is better for designing measurement systems on a previously unmonitored structure while the greedy algorithm is better for incremental measurement-interpretation strategies.