The aim of this study was to compare the predictive performance of ridge regression best linear unbiased prediction-method 6 (rrBLUPm6) with well-known genomic selection methods (rrBLUP, GBLUP and BayesA) in terms of accuracy of prediction, computing time and memory requirement. The impact of the genetic architecture and heritability on the accuracy of genomic evaluation was alsostudied. To this end, a genome was simulated which consisted of five chromosomes, one Morgan each, on which 5000 biallelic single nucleotide polymorphisms (SNP) were distributed. Prediction of genomic breeding values was done in different scenarios of number of QTL (50 and 500 QTL), distribution of QTL effects (uniform, normal and gamma) and different heritability levels (0.1, 0.3 and 0.5).Pearson’s correlation between true and predicted genomic breeding values (rp,t) was used as the measure of prediction accuracy. Computingtime and memory requirement were also measured for studied methods. The accuracy of rrBLUPm6 was higher than GBLUP and rrBLUP, and was comparable with BayesA. In addition, regarding computing time and memory requirement, rrBLUPm6 outperformed other methods and ranked first. A significant increase in accuracy of prediction was observed following increase in heritability. However, the number and distribution of QTL effects did not affect the accuracy of prediction significantly. As rrBLUPm6 showed a great performance regarding accuracy of prediction, computing time and memory requirement, we recommend it for genomic selection.