Systematic analysis of the genetic background of complex diseases using single nucleotide polymorphisms (SNPs) affords a tremendous amount of genotypings. To reduce the amount of genotypings necessary and hence the overall cost of a case-control study with SNPs, the genotyping is often performed in two stages. In the first, the DNA of all cases and all controls are mixed into two pools and genotyped for each SNP. The frequency of both alleles is determined in both pooled DNA samples. If different frequencies are observed in the pools of cases and controls, genotyping is performed individually in the second stage and analyzed conventionally. However, so far no well-founded algorithm is available to guide the decision on whether to genotype a SNP individually. In this report, an approach is introduced for the decision on individual genotyping based on the results from pooled DNA. The analysis is modeled as a decision process with the specific goal to decide on whether to genotype a specific SNP individually. For a given situation, the resulting decision criteria are aimed to be optimal for those conducting the study. Different loss functions and decision rules are presented. Using Monte-Carlo simulations, we show that for a given situation, the genotyping rates and hence the costs can be reduced remarkably while maintaining acceptable overall error rates.