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Given the model of a system with explicit temporal constraints, optimal temporal planning is the problem of finding a schedule of actions that achieves a certain goal while optimizing an objective function. Recent approaches for optimal planning reduce the problem to a series of queries to an Optimization Modulo Theory (OMT) solver: each query encodes a bounded version of the problem, with additional abstract actions representing an over-approximation of the plans beyond the bound. This technique suffers from performance issues, mainly due to the looseness of the over-approximation, which can include many non-executable plans. In this paper, we propose a refined abstraction for solving optimal temporal planning via OMT by introducing abstract scheduling constraints, which have a double purpose. First, they enforce a partial ordering of abstract actions based on mutual dependencies between them, which leads to a better makespan estimation and allows to prove optimality sooner. Second, they implicitly forbid circular self-enabling of abstract actions, which is a common cause of spurious models that severely affects performance in existing approaches. We prove the soundness and completeness of the resulting approach and empirically demonstrate its superiority with respect to the state of the art.