Co-occurrences of peptides and chemical components are usually observed in complicated matrices. Notably, those traditional Chinese medicine prescriptions (TCMPs) contain both plant and animal ingredients. It is still challenging to simultaneously monitor peptides and chemical components attributing to their different liquid chromatographic (LC) and mass spectrometric (MS) behaviors. Herein, efforts were made to pursue an eligible approach enabling simultaneous determination of peptides and chemical components in a TCMP namely Cervus and Cucumis polypeptide injection (CCPI, Songmeile®) that is prepared from the acid hydrolytic peptide-enriched extract of Sika deer (Cervus nippon Temminck) bone and the aqueous extract of muskmelon (Cucumis melo L.) seeds. Reversed phase liquid chromatography and hydrophilic interaction liquid chromatography were serially connected (RPLC-HILIC) to achieve comprehensive retention and separation. Sensitive detection was accomplished with selected reaction monitoring (SRM) mode, and multiply charged and singly charged ion transitions were defined for peptides and chemical components, respectively. Inter-batch variations of CCPI were evaluated in an authentic compound-independent manner. In particular, online energy-resolved MS was proposed to gain optimal parameters for five targeted peptides after that CCPI peptidome was profiled using nanoLC-LTQ Orbitrap Velos Pro MS. A so-called universal metabolome standard (UMS) sample was built for calibration curve construction and subsequently applied to acquire the quasi-contents of all 31 analytes, including five peptides and 26 chemical components, in ten batches of CCPI (CCPI1–CCPI10). The quantitative dataset revealed mild fluctuation for the quasi-content profiles of analytes-of-interest within different batches. More importantly, RPLC-HILIC–SRM is a promising method to fully address the demands of simultaneous measurement of peptides and chemical components in complicated matrices, and it might be a robust analytical tool for in-depth quality evaluation of CCPI as well as other TCMPs.