We describe a probabilistic approach to siting samplers for detecting accidental or intentional releases of biological material. In the face of uncertainty and variability in the release conditions, we place samplers in order to maximize the probability of detecting a release from among a suite of realistic scenarios. The scenarios may differ in any unknown, for example, the release size or location, weather, mode of building operation, etc. In an illustrative example, we apply the algorithm to a hypothetical 24-room commercial building, finding optimal networks for a variety of assumed sampler types. The results show how sampler characteristics, most importantly the detection limit, affect the network performance. This suggests using the probabilistic approach to guide the priorities of sampler designers, as well as to site samplers in specific buildings.