In view of problems such as field poor shock resistance, low target identification rate, low real-time and so on in mechanical scanning optical system, a non-scanning target identification remote sensing system was designed using the multi-spectrum separation algorithm. Using the non-scanning M-Z interferometer to provide a space optical path difference, interference fringes were collected by infrared CCD detector. After CUP processing the system obtains the mix spectrum information, achieves target identification by the coordinate system combined with visible light video image, and the coordinate system, which the union visible light video image provides, achieves the target discrimination. The genetic algorithm was used to optimize characteristic wavelengths, and then by the rough collection classification the unknown target spectrum’s attribute was extracted. Taking first 1/3 confidence level of the corresponding attribute the testing target type was deduced, and compared with the traditional algorithm the amount of computing was reduced by about nine times. Experiment was done under different weather and different background conditions, so detection limits and identification probabilities of the system under different conditions were obtained. The experimental data showed that the genetic algorithm and rough set classification combined with multi-spectral separation algorithm can quickly and efficiently identify the unknown object types.