A utility-function approach to optimal spatial sampling design is a powerful way to quantify what "optimality" means. The emphasis then should be to capture all possible contributions to utility, including scientific impact and the cost of sampling. The resulting sampling plan should contain a component of designed randomness that would allow for a non-parametric design-based analysis if model-based assumptions were in doubt.
Cressie, N. A. & Chambers, R. L. (2015). Comment on article by Ferreira and Gamerman. Bayesian Analysis, 10 (3), 741-748.