In this thesis, Real-coded Genetic Algorithm (RGA) is used to explore the Inverse Kinematics (IK) issues for the six-axis robotic manipulators. The main study focuses on the simulation of two groups of six-axis robotic manipulators to reach a given position and orientation of the end-effector. The tasks are to evaluate robot joint parameters for these point-to-point trajectory planning problems. In order to simulate the motion of each axis for the trajectory planning process of these six-axis robotic manipulators. AutoDesk Inventor and Java OpenGL techniques were used to develop a web-based six-axis robotic manipulator simulation system. This system can accept manipulator's end-effector position and orientation inputs through web browser, and then followed with back-ends computation to solve the associated inverse kinematics problem. Once the computation complete, system will notify user with e-mail to investigate the simulation result. The implemented web-based six-axis robotic manipulator simulation system allows user to replace parts for specific assembly. All system parts are saved in ASCII STL (Stereo Lithography) file format, and registered in the database. User can apply these parts to assemble customized six-axis robotic manipulator and conduct associated inverse kinematics analysis and simulation. To verify the usefulness of this system, three cases for each manipulator systems which consist of various revolute and prismatic joints were studied. All results from RGA methods with minimizing trajectory errors reached precise key points locating right on the target trajectory to justify the credibility of this study. This research also applies curve fitting method to approximate the continuous curve for each driving axis. Primarily result shows that more key points on the target end-effector position and orientation inputs will gain better accuracy for the associated inverse kinematics analysis.