Hardware design involves considerations of the safety, portability, and flexibility of the mechanism to achieve effective rehabilitation for the patient [17]. Finger movement characteristics are essential in device design to specify patient safety. Large devices need hospitals to be equipped with ample rooms to place them. They are also expensive, which reduces the number of units purchased, leading to fewer patients who can use this technology [17]. Exoskeleton-type devices such as those presented in [20; 21] require little space because they are mounted on the patient's limb. Usually, the exoskeleton-type devices present more mechanically complex designs because the human joints must coincide with the exoskeleton's joints. Furthermore, that can involve more than one degree of freedom (DOF) in the device. End-effector-type devices such as Amadeo [22], a leading finger rehabilitation device, features a simple and versatile design that easily adjusts to various hand dimensions, however, it needs a large work area to operate. The challenge with the end-effector devices is to make the designs as small as possible to obtain better portability. From [14], only four devices focus on finger rehabilitation. In [15], seven are end-effector devices for finger rehabilitation, two of which are mentioned in [14]. In [16], they mentioned four systems assisting finger movements, two also mentioned in [14] and one in [15]. The end-effector developments are fewer in comparison with exoskeleton development, resulting in a potential field of research. Optimization of mechanical and robotic designs is widely used for various applications such as serial manipulators and parallel robots [23; 24; 25]. However, it has also gained importance in assistive and rehabilitation devices as well as exoskeleton systems [26; 27; 28; 29; 30; 31]. New optimization techniques have been sought to improve the dimensional characteristics and dynamic characteristics of the systems. One of the methods that has gained more attention in the optimization of mechanisms is the genetic algorithm. Due to the quality of the population produced, only the best individuals in each iteration are taken into account. The authors present the optimization of an end-effector finger rehabilitation device that reproduces the individual trajectory of the fingers, which contributes to the first stage of rehabilitation (assistance for passive movements). Compared to other end-effector devices, the presented rehabilitation mechanism does not imitate the hand's grasping functions. Ensuring repetitive monitoring of the natural flexion-extension trajectory contributes to proper fit and alignment of the finger joints. The patient recovers the joint mobility simultaneously (MP, PIP, and DIP). Respecting each joint's range of movement can contribute to the gross motor function and fine motor function. From a rehabilitation viewpoint, to improve the range of movement (ROM) at a joint, each joint must be moved through inside its ROM (expressed in degrees) at regular intervals. A continuous passive motion device (like a rehabilitation robot) facilitates the rapid recovery of the neuromuscular system by improving ROM [8; 9; 32; 33]. The goal of this article is the proposal of an optimal redesign for a finger rehabilitation device. The work focused on proposing the optimal dimensions of the existing mechanism's links, allowing for more accurate tracking of a trajectory compared to the existing prototype that uses a screw-nut transmission. Additionally, the original prototype's links are too big concerning the end-effector trajectory's size, which decreases the portability of the prototype. The rest of this paper is structured as follows. Section 2 contains the finger rehabilitation device's description and kinematic model and presents the characterization and analysis of the real finger trajectories, and finally, the optimization problem. Section 3 presents the optimization results, Section 4 presents the discussion, and, in Section 5, the conclusions are presented.