A major issue faced by breeders is how to effectively manage adverse correlations in breeding programs. We present results of a Monte Carlo allele-based simulation of the changes in response and variance of response under adverse genetic correlations by using the examples of two contrasting selection methods: the 'Smith-Hazel' selection index (SH) and independent culling (IC). We assumed several gene models, which included linkage and antagonistic pleiotropy as the primary drivers of adverse genetic correlations. The different behaviors of these selection methods allowed us to identify the mechanism behind the generation of uncertainty under antagonistic trait selection: IC had the properties of stabilizing selection, while SH behaved more similar to disruptive selection. Although SH outperformed IC in terms of genetic gain, this advantage happened at the cost of higher variance of response and loss of heterozygosity. Using an optimum selection algorithm (OS) to prevent the loss of heterozygosity through a constraint on inbreeding in SH/OS increased marginally the reliability, remaining still below that of IC under equal conditions. However, SH/OS had lower inbreeding (ΔF) than IC for equivalent levels of genetic gain, so a compromise between high selection reliability, low ΔF, and gain must be made by a breeder under antagonistic trait selection even with the use of optimization tools.