The modern vehicle electrical architecture consists, on average, of 30 integrated electronic modules (ABS, infotainment, instrument panel, etc.), also known as Electronic Control Units (ECUs), and approximately 300 peripherals such as sensors (collision, temperature, oxygen, position, pressure, etc.) and actuators (window motor, mirror motor, relays, airbag inflator, windshield wiper, etc.). This increase in component integration imposes significant challenges to system installation and design. The interconnection of multiple devices renders harness design an arduous and time-consuming task, especially when conducted manually, resulting in error-prone and suboptimal outcomes. Such a scenario highlights the pressing need for studies on harness routing optimization in the automotive industry. Historically, wiring harness design practices have transitioned from manual approaches to the adoption of advanced computational tools. This methodological transition encompasses the use of various techniques, such as algorithms, 3D simulation, and machine learning, aiming for effective solutions to this complex challenge. In this context, the present work aims to conduct a literature review on wiring harness routing optimization strategies, with an emphasis on their application in vehicular electrical architecture. The current academic literature indicates that advancements in optimization approaches are crucial, especially through the application of methods such as Genetic Algorithms, Agent-Based Modeling and Simulation, Integer Linear Programming (ILP) and Linear Programming (LP) applied to the Steiner Tree Problem, Simulated Annealing, Ant Colony Systems, Particle Swarm, among others. Such methodologies are fundamental not only for developing lighter and more compact harnesses but also for a more efficient exploration of available physical space, culminating in layout development time optimization.