We consider the problem of design of a network of observation locations in a spatial domain that will be used to interpolate a spatial field. We compare design strategies based on simultaneous simulated annealing [1, 3] and sequential point selection algorithms  using criteria such as minimum estimation variance (MEV), maximum conditional variance (MCV)  and maximum entropy [5, 6]. Designs are evaluated using underlying probabilistic random field models that are both stationary and non-stationary and stratified random fields. We present an example of optimal network design for measuring maximum temperatures in Mojave desert. We propose an improved adaptive annealing strategy. The search for the optimal set of locations concentrates on the possible minima of the criterion function with increasing probability. We utilize prior knowledge at each step of the selection process, insuring better convergence rates to the global minimum.