The situation of heavy metal pollution in farmland isn’t optimistic. The heavy metals in soil can affect normal growth and development of crops after being absorbed by the roots, reduce quality of agricultural products, and then enter human body through food chain, endangering human health. Hyperspectral Remote Sensing provides possibility for a real-time, dynamic and efficient monitoring of heavy metal pollution in crops. The potted corn experiment with different Cu2+ stress gradients was set up, the spectral data of old, middle and new leaves in seedling, jointing and spike stages were collected, and the chlorophyll content and leaves Cu2+ content were determined in different growth periods. Based on the spectral data, chlorophyll content and leaves Cu2+ content, OIF-PLS method was constructed to extract feature bands containing Cu2+ pollution information by combining correlation analysis, optimal index factor (OIF) and partial least square (PLS). Firstly, the characteristic bands were preliminarily screened according to correlation coefficient between chlorophyll content in leaves at seedling stage, jointing stage and spike stage and Cu2+ content in leaves at spike stage and corresponding leaf spectra. Then, three bands were selected to calculate optimum index factor, and the three bands were taken as independent variables to carry out partial least squares regression analysis on Cu2+ content in corn leaves to calculate root mean square error. Finally, the best feature band was selected according to principle of maximum optimum index factor and minimum root mean square error. The vegetation index OIFPLSI was constructed based on the characteristic bands selected by OIF-PLS method to monitor heavy metal copper pollution, and compared with red edge normalized difference vegetation index (NDVI705), modified red edge simple ratio vegetation index (mSR705), red-edge vegetation stress index(RVSI) and photochemical reflectance index (PRI) monitoring results to verify the effectiveness and superiority of OIFPLSI. In addition, the applicability and stability of OIFPLSI were verified by using the data obtained from different years under same experimental method. The experimental results show that the feature bands (542, 701, 712 nm) extracted from OIF-PLS can better reflect Cu2+ pollution information than the feature bands (602, 711, 712 nm) based on OIF. OIFPLSI was significantly positively correlated with leaf Cu2+ content, and the correlation was better than NDVI705, mSR705, RVSI and PRI. OIFPLSI was significantly negatively correlated with leaf chlorophyll content and positively correlated with Cu2+ content in soil. The correlation between OIFPLSI and Cu2+ content in soil at different growth stages is successively higher in jointing stage, ear stage and seedling stage. Based on the data of different years, the results show that OIFPLSI is positively correlated with leaf Cu2+ content, and OIFPLSI has strong stability. OIFPLSI based on the characteristic bands extracted by OIF-PLS method can better diagnose and analyze copper pollution level of corn leaves, which can provide a certain technical reference for crop heavy metal pollution monitoring.