Abstract: An improved self-calibrating method for visual servo based on enhanced mutative scale chaos optimization algorithm (EMSCOA) is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi's self-calibration for position-based visual servo technique which exploits the singular values property of the essential matrix. Specifically, a suitable dynamic online cost function is generated and minimized using an EMSCOA instead of gradient descent method. First, the chaos variables are mapped to the range of the five intrinsic parameters, and then two cycles are set, in which chaos search is performed in the inner cycle and the range is reduced in the outer cycle, in order to avoid blind and repeated searching of chaos optimization in searching space and improve searching efficiency. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value, which is not constant in many cases, and the true value is approached by setting the bounds of the unknown parameters off-line. Besides, this algorithm does not require knowledge of the 3D model of the object. Simulation experiments are carried out and the results demonstrate that the proposed approach provides fast convergence speed and is robust against unpredictable perturbations of camera parameters. Experiment result also has verified the effectiveness of the proposed algorithm.