Multi-omics data analysis is about integrating multiple omics data and create strong and complex models to extract deep relations between an organism's genotype and phenotype. The aim of this study is to propose a novel multi-objective evolutionary massive integration method (EMI) for the classification problem of multi-omics data. In this method, first, we have created massive Support Vector Machine (SVM) based models with random features from each omics data type. Then, using an evolutionary algorithm, a combined model of numerous models is created to satisfy two objectives. The first objective of the proposed method is to improve accuracy of the classification problem and the second objective is to find informative biomarkers related to target phenotype. To validate the proposed method, we have gathered breast cancer multi-omics data from The Cancer Genome Atlas (TCGA). We have used estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes. The goal of this study is to predict these subtypes. The proposed method outperforms state of the art articles in this area of research in terms of accuracy and area under curve. Another contribution of this paper is the introduction of some new biomarkers that related to breast cancer subtypes.