Genomic selection has great potential to increase the efficiency of plant breeding, but its implementation is hindered by the high costs of collecting the necessary data. In this study we evaluated the potential of accurate within-family imputation for enabling cost-effective genomic selection. We have simulated a breeding program with inbred parents and their segregating progeny distributed among families, of which some were used as a training set and some were used as a prediction set. Parents were genotyped at high-density (20,000 markers), while progeny were genotyped at high-density or low-density (500, 200, 100, or 50 markers) and imputed. Low-density markers were chosen to segregate within each family separately. The assumed low-density genotyping costs accounted for this assumption. Six sets of scenarios were analysed in which imputation was leveraged to maximize cost-effectiveness of genomic selection by i) decreasing the genotyping costs, ii) increasing selection intensity by genotyping more individuals at fewer markers, or iii) increasing prediction accuracy by genotyping more phenotyped individuals at fewer markers. The results show that with a constant size of the training and prediction sets the prediction accuracy was unimpaired when at least 200 low-density markers were used. However, the return on investment was maximal (5.67 times that of the baseline scenario) when only 50 low-density were used, because that enabled maximal reduction in the genotyping costs and only minimal reduction in the prediction accuracy. Increasing either the training set or prediction set further increased the return on investment when imputed genotypes were used, but not when the true high-density genotypes were used. The results show how plant-breeding programs can implement genomic selection in a cost-effective way.