At present, the most common cause of cancer-related death in women is breast cancer. In a large proportionof breast cancers, there is the overexpression of human epidermal growth factor receptor 2 (HER2). This receptoris a 185 KDa growth factor glycoprotein, also known as the first tumor-associated antigen for different typesof breast cancers. Moreover, HER2 is an appropriate cell-surface specific antigen for passive immunotherapy,which relies on the repeated application of monoclonal antibodies that are transferred to the patient. However,vaccination is preferable because it would stimulate a patient’s own immune system to actively respond to adisease. In the current study, several bioinformatics tools were used for designing synthetic peptide vaccines.PEPOP was used to predict peptides from HER2 ECD subdomain III in the form of discontinuous-continuousB-cell epitopes. Then, T-cell epitope prediction web servers MHCPred, SYFPEITHI, HLA peptide motif search,Propred, and SVMHC were used to identify class-I and II MHC peptides. In this way, PEPOP selected 12discontinuous peptides from the 3D structure of the HER2 ECD subdomain III. Furthermore, T-cell epitopeprediction analyses identified four peptides containing the segments 77 (384-391) and 99 (495-503) for both Band T-cell epitopes. This work is the only study to our knowledge focusing on design of in silico potential novelcancer peptide vaccines of the HER2 ECD subdomain III that contain epitopes for both B and T-cells. Thesefindings based on bioinformatics analyses may be used in vaccine design and cancer therapy; saving time andminimizing the number of tests needed to select the best possible epitopes.