A general methodology for designing sampling schemes for monitoring is illustrated with a case study aimed at estimating the temporal change of the spatial mean P concentration in the topsoil of an agricultural field after implementation of the remediation measure. A before-after control-impact (BACI) sample-pattern is proposed, with stratified random sampling as a spatial sampling design. The strata are formed as compact blocks of equal area, so that the sample locations cover the field very well. Composite sampling, where the aliquots of a composite come from different strata, is proposed in order to save laboratory costs. The numbers of composites and aliquots per composite are optimized for testing the hypothesis that the mean P concentration didn't change or has increased. Initially, this is done for a known variogram, temporal correlation, variance of laboratory measurement error, initial mean P concentration, and time needed for fieldwork. The optimal sample size to achieve a power of 0.90 at a 10% decrease of the mean P concentration is six composites of six aliquots each. Next, the effect of uncertainty about these model parameters on the optimal sample size and on the power of the test for a fixed sample size is analyzed. This analysis showed that, to obtain a probability of 95% that the power ¿ 0.90, the sample size must be increased to 7 composites of 10 aliquots each.