Tocochromanols (vitamin E) are an essential part of the human diet. Plant products including maize grain are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel) and an exotic-derived (BGEM) maize population. While prediction accuracies up to 0.79 were achieved using gBLUP when predicting within each population, genomic prediction of BGEM based on an Ames Diversity Panel training set resulted in low prediction accuracies. Optimal training population (OTP) design methods FURS, MaxCD, and PAM were adapted for inbreds and, along with the methods CDmean and PEVmean, often improved prediction accuracies compared to random training sets of the same size. When applied to the combined population, OPT designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.