The objective of this paper is to introduce a new methodology for the spatial sampling design problem in the case where the variable of interest cannot be directly observed, but information on it can be obtained by sampling a related variable, and estimation for the underlying model is required. Assuming a certain model for the involved variables is given, an approach based on entropy has been proposed by Bueso, Angulo and Alonso (1997). However, in some cases a predetermined structure modelling the behaviour of the variables cannot be assumed. In this context, we derive criteria for solving the design problem, based on the stochastic complexity theory and on the philosophy of the EM algorithm. For applying the proposed criteria a computational procedure is developed based on the supplemented EM algorithms. The methodology is illustrated in a simple case.