Computational tool-assisted primer design for real-time reverse transcription (RT) PCR (qPCR) analysis largely ignores the sequence similarities between sequences of homologous genes in a plant genome. It can lead to false confidence in the quality of the designed primers, which sometimes results in skipping the optimization steps for qPCR. However, the optimization of qPCR parameters plays an essential role in the efficiency, specificity, and sensitivity of each gene’s primers. Here, we proposed an optimized approach to sequentially optimizing primer sequences, annealing temperatures, primer concentrations, and cDNA concentration range for each reference (and target) gene. Our approach started with a sequence-specific primer design that should be based on the single-nucleotide polymorphisms (SNPs) present in all the homologous sequences for each of the reference (and target) genes under study. By combining the efficiency calibrated and standard curve methods with the 2^−ΔΔCt method, the standard cDNA concentration curve with a logarithmic scale was obtained for each primer pair for each gene. As a result, an R² ≥ 0.9999 and the efficiency (E) = 100 ± 5% should be achieved for the best primer pair of each gene, which serve as the prerequisite for using the 2^−ΔΔCt method for data analysis. We applied our newly developed approach to identify the best reference genes in different tissues and at various inflorescence developmental stages of Tripidium ravennae, an ornamental and biomass grass, and validated their utility under varying abiotic stress conditions. We also applied this approach to test the expression stability of six reference genes in soybean under biotic stress treatment with Xanthomonas axonopodis pv. glycines (Xag). Thus, these case studies demonstrated the effectiveness of our optimized protocol for qPCR analysis.