A MS method is described here that can reproducibly identify hundreds of peptides across multiple experiments. The method uses intelligent data acquisition (IDA) to precisely target peptides while simultaneously identifying thousands of other, non-targeted peptides in a single nano-LC-MS/MS experiment. We introduce an online peptide elution order alignment (EOA) algorithm that targets peptides based on their relative elution order, eliminating the need for retention time-based scheduling. We have applied this method to target 500 mouse peptides across six technical replicate nano-LC-MS/MS experiments and were able to identify 440 of these in all six, compared to only 256 peptides using data-dependent acquisition (DDA). A total of 3,757 other peptides were also identified within the same experiment, illustrating that this hybrid method does not eliminate the novel discovery advantages of DDA. The method was also tested on a set of mice in biological quadruplicate and increased the number of identified target peptides in all four mice by over 80% (826 vs. 459) compared with the standard DDA method. We envision real-time data analysis as a powerful tool to improve the quality and reproducibility of proteomic datasets.