Currently, most pairwise resolution models for event co-reference focused on classification or clustering approaches, which ignored the relations between events in a document. A global optimization model for event co-reference resolution was proposed to resolve the inconsistent event chains in classifier-based approaches. This model regarded co-reference resolution as a integer linear program problem and introduced various kinds of constraints, such as symmetry, transitivity, triggers, argument roles, event distances, to further improve the performance. The experimental results show that the proposed model outperforms the local classifier by 4.20% in F1-measure.