Aiming to solve the problems of large-scale resources， complex constraints， and difficult cooperation of multi-timescale decision-making in the crude oil scheduling process， a Multi-Timescale Cooperation Evolutionary Algorithm （MTCEA） was proposed. Firstly， a large-scale multi-timescale crude oil scheduling optimization model was established according to the scale structure and actual demand of oil refining enterprises， which consists of a resource-oriented medium- and long-term scheduling model and an operation-oriented short-term scheduling model， and achieves a reasonable allocation of crude oil resources through employing a dynamic grouping strategy of crude oil resources to satisfy the requirements of different scheduling scales， multi-timescale characteristics， and fine production. Secondly， to promote the integration of scheduling decisions at different time scales， an evolutionary algorithm based on multi-timescale cooperation was designed and solved by constructing subproblems for the continuous decision variables in scheduling models at different time scales to achieve cooperation optimization between scheduling decisions at different time scales. Finally， MTCEA was verified in three practical industrial cases. Compared with three representative large-scale evolutionary optimization algorithms （i.e.， Competitive Swarm Optimizer （CSO）， Self-adaptive Differential Evolution with Modified Multi-Trajectory Search （SaDE-MMTS）， and Mixture Model-based Evolution Strategy （MMES）） and three high-performance Mixed Integer Non-Linear Programming （MINLP） mathematical solvers （ANTIGONE （Algorithms for coNTinuous/Integer Global Optimization of Nonlinear Equations）， SCIP （Solving Constraint Integer Programs）， and SHOT （Supporting Hyperplane Optimization Toolkit））， the results show that the metrics of the solution optimality and stability of MTCEA are improved by more than 30% and 25%， respectively. These significant performance improvements demonstrate the practical application value and advantages of MTCEA in large-scale multi-timescale crude oil scheduling decisions.