In fine mapping of a large-scale experimental population where collection of phenotypes are very expensive, difficult to record or time-demanding, selective phenotyping could be used to phenotype the most informative individuals. Linkage analyses based sampling criteria (LAC) and linkage disequilibrium-based sampling criteria (LDC) for selecting individuals to phenotype are compared to random phenotyping in a quantitative trait loci (QTL) verification experiment using stochastic simulation. Several strategies based on LAC and LDC for selecting the most informative 30%, 40% or 50% of individuals for phenotyping to extract maximum power and precision in a QTL fine mapping experiment were developed and assessed. Linkage analyses for the mapping was performed for individuals sampled on LAC within families and combined linkage disequilibrium and linkage analyses was performed for individuals sampled across the whole population based on LDC. The results showed that selecting individuals with similar haplotypes to the paternal haplotypes (minimum recombination criterion) using LAC compared to random phenotyping gave at least the same power to detect a QTL but decreased the accuracy of the QTL position. However, in order to estimate unbiased QTL parameters based on LAC in a large half-sib family, prior information on QTL position was required. The LDC improved the accuracy to estimate the QTL position but not significantly compared to random phenotyping with the same sample size. When applying LDC (all phenotyping levels), the estimated QTL effect were closer to the true value in comparison to LAC. The results showed that the LDC were better than the LAC to select individuals for phenotyping and contributed to detection of the QTL.