Cloud computing has revolutionized access to computational resources, but efficiently scheduling workflows remains challenging due to conflicting objectives of minimizing response time and reducing energy consumption. This paper proposes an Adaptive Multi-Objective Workflow Scheduler (AMOWS) based on Particle Swarm Optimization (PSO) to address these challenges. The scheduler employs a multi-objective function, balancing response time and energy consumption, followed by a task prioritization strategy before final scheduling. Implemented using Java and CloudSim, our comprehensive simulation demonstrates the effectiveness of AMOWS against existing heuristic and hybrid approaches. Results show significant improvements in both response time (up to 53.9% reduction) and energy efficiency (up to 68.8% reduction) across various virtual machine configurations. The findings highlight the trade-offs among performance metrics and establish AMOWS as a promising solution for dynamic cloud workflow scheduling.