A methodology to design a model free cruise control algorithm(MFCC) is presented in this paper. General cruise control algorithms require lots of vehicle parameters to control the power train and the brake system, that makes control system complicate. Moreover, when the target vehicle is changed, the vehicle parameters should be reinvestigated in order to apply the cruise control algorithm to the subject vehicle. To overcome these disadvantages of the conventional cruise control algorithm, MFCC algorithm has been developed. The algorithm directly determines the throttle, brake inputs based on the reference model parameters such as clearance, relative velocity, and subject vehicle acceleration. This simple structure facilitates human centered design of cruise controller and makes it easy to apply control algorithm to various vehicles without reinvestigation of vehicle parameters. To achieve vehicle safety and driver comfort, control parameters of the model free cruise control algorithm has been designed using the real-world driving test data. Few parameters have been adapted using a learning algorithm to minimize the effects of unawareness of the vehicle parameters. From the simulation study, the performance of the proposed controller has been compared with that of the conventional adaptive cruise controller(ACC). It is shown that the proposed control strategy its behavior is not only similar to that of the conventional ACC in normal driving condition but also more robust than the ACC for model uncertainty and external disturbance.