A predictive design method for synthetic ribosome binding sites (RBSs) enables precise control of protein expression in microbial engineering. The method, validated with over 100 experiments in *E. coli*, accurately predicts translation initiation rates within a 2.3-fold range. It accounts for factors like RBS sequence, mRNA secondary structure, and spacing between the RBS and start codon. The method uses a thermodynamic model to calculate the free energy changes associated with translation initiation, enabling the design of RBS sequences that achieve desired protein expression levels. The model incorporates interactions between the 30S ribosomal subunit and mRNA, including hybridization of the 16S rRNA to the RBS, tRNA binding, and RNA secondary structures. The model was tested with synthetic RBSs and demonstrated a linear relationship between predicted free energy changes and protein fluorescence. The method was further used to optimize RBS sequences for specific protein expression levels, enabling the rational design of genetic circuits. The approach allows for the systematic optimization of large genetic systems by predicting RBS sequences that achieve target translation initiation rates. The method was applied to connect a genetic sensor to a synthetic circuit, demonstrating its utility in engineering complex biological systems. The thermodynamic model and optimization algorithm were validated with experiments showing a strong correlation between predicted and measured protein expression levels. The method's accuracy was confirmed across a wide range of protein expression levels, making it a valuable tool for synthetic biology applications.A predictive design method for synthetic ribosome binding sites (RBSs) enables precise control of protein expression in microbial engineering. The method, validated with over 100 experiments in *E. coli*, accurately predicts translation initiation rates within a 2.3-fold range. It accounts for factors like RBS sequence, mRNA secondary structure, and spacing between the RBS and start codon. The method uses a thermodynamic model to calculate the free energy changes associated with translation initiation, enabling the design of RBS sequences that achieve desired protein expression levels. The model incorporates interactions between the 30S ribosomal subunit and mRNA, including hybridization of the 16S rRNA to the RBS, tRNA binding, and RNA secondary structures. The model was tested with synthetic RBSs and demonstrated a linear relationship between predicted free energy changes and protein fluorescence. The method was further used to optimize RBS sequences for specific protein expression levels, enabling the rational design of genetic circuits. The approach allows for the systematic optimization of large genetic systems by predicting RBS sequences that achieve target translation initiation rates. The method was applied to connect a genetic sensor to a synthetic circuit, demonstrating its utility in engineering complex biological systems. The thermodynamic model and optimization algorithm were validated with experiments showing a strong correlation between predicted and measured protein expression levels. The method's accuracy was confirmed across a wide range of protein expression levels, making it a valuable tool for synthetic biology applications.