The rapid growth of large-scale machine learning (ML) workloads has led to unprecedented demands on GPU cluster scheduling systems. However, existing heuristic schedulers struggle to handle the complexity and dynamics of multi-cluster GPU environments, often leading to load imbalance, long queuing delays, and poor alignment to business objectives. This paper formulates the ML scheduling problem in multi-cluster GPU environments as online multi-objective optimization that minimizes the average and maximum weighted queuing times while maximizing GPU time utilization. To solve this optimization problem, we propose EVO-GP, a two-layer evolutionary online scheduling method that decouples global job-to-cluster allocation from intra-cluster GPU resource allocation. At the job-to-cluster allocation layer, an evolutionary algorithm assigns arriving jobs to feasible clusters to promote load balance across clusters. At the resource allocation layer, a priority function automatically learned offline via genetic programming is used to rank jobs and guide GPU allocation within each cluster. Extensive experiments on both real-world data and simulated workloads demonstrate that the proposed EVO-GP method consistently outperforms state-of-the-art baselines, while maintaining superior adaptability and scalability.

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