Satellites may provide data with various spectral and spatial resolutions. The spatial resolution of panchromatic (PAN) images is higher, but the spectral resolution of multispectral (MS) images is greater. There is Satellite sensors limitation for capturing an image with high spatial and spectral resolution, due to the hardware design of the sensors. Whereas many remote sensing, as well as GIS applications, need high spatial and spectral resolution. Image fusion merges images of different spectral and spatial resolutions based on a certain algorithm. It can be used to overcome the sensor's limitation and play an important role in the extraction of information. The standard image fusion approaches lose spatial information or distort spectral characteristics. Optimizations of fusion rules can overcome and degrade the distortions as the fusion core is the image fusion rules. In this paper, the Grey Wolf Optimizer (GWO) is used to find the optimal injection gain, as most distortions in image fusion are caused by the extraction and injection of spatial detail. Both qualitative and quantitative metrics were utilized to evaluate the quality of the merged image. The mentioned metrics that were used commonly for evaluation of image fusion results support the proposed algorithm for image fusion as the output image was qualitatively and quantitatively growth. In the future the proposed method can be updated by increasing the objective function dimensions to two or three for getting a best fused image.