In this paper, we discuss stability and accuracy of target detection process in infrared images, using continuous features of the target or target candidates, and we improve the accuracy of target detection by optimizing an evaluation function. This process carries out parallel image process. The one, Two-dimensional Constant False Alarm Rate (CFAR) process reduces background clatter. The other, Motion Vector Process detects moving target. In addition, Combined Target Detection Process improves the accuracy of target detection using features from two different processes. Continuous and stable data measurement is necessary to improve the accuracy of target detection. But measurement data have noises and fluctuations by change of the environment. In this case, we use continuous features to stable detection. On the other hand, optimizing weight vectors in evaluation function is necessary to improve target detection. But we have to deal with large number of parameters. In this optimization named Combined Target Detection Process, we use Genetic Algorithms (GA) to get a global optimum of parameters. This process is useful for outdoor surveillance systems, intelligent transport systems (ITS) and so on.