We present a systematic evaluation of optimization strategies for high-throughput organic photovoltaic (OPV) manufacturing. Analyzing 11,587 PBF-QxF:Y6 devices across 11 manufacturing parameters through 25 optimization iterations, we compared Bayesian Optimization (BO) and Random Search (RS). While BO achieved 7.69% PCE versus RS's 7.66%, this 0.03% advantage required 20x more computational overhead. Statistical analysis revealed no significant performance difference between methods (t-stat = 0.53, p > 0.05). Environmental factors, particularly humidity (r = 0.380), showed stronger correlation with performance than optimization strategy choice. Manufacturing process control, rather than algorithmic sophistication, emerges as the critical factor for high-throughput OPV optimization. These findings suggest prioritizing robust process control systems over complex optimization algorithms in manufacturing environments.