Waveform Relaxation (WR) is a technique for solving large systems of ordinary differential equations (ODEs) by decomposing them into smaller, parallelizable subsystems. While Classical WR (CWR) can suffer from slow convergence, Optimized Waveform Relaxation (OWR) enhances performance by introducing a free parameter that improves information flow between subsystems. However, OWR research has primarily concentrated on continuous formulations and frequently neglects the role of temporal discretization, which is crucial for practical applications. This study shifts to the discrete level, investigating OWR effectiveness in time dependent problems utilizing transmission line circuits. We analyze how temporal discretization impacts OWR convergence, providing insights that could enhance its practical application in large-scale computational problems.