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There are many uncertain factors in the optimal allocation of water resources in irrigated areas, while the optimization models considering the uncertainties are often faced with the problems of complex structure, limited uncertain parameters, low calculation accuracy and efficiency. Therefore, a method for parameter sensitivity analysis of irrigation water optimization model as well as uncertainty optimization was developed through coupling the Latin hypercube-One factor at a time (LH-OAT) method with an irrigation water optimization model. Taking a typical irrigation district in the middle reaches of the Heihe River basin as the case study area, the sensitivity analysis method was conducted for 25 uncertainty parameters from six categories parameters of the model, and the uncertainty optimization of irrigation water use was then realized based on the highly sensitive parameters. The sensitivity ranking of 25 uncertainty parameters in the model was calculated, and 10 highly sensitive parameters were selected. Taking the highly sensitive parameters as uncertainty parameters input for the optimization model, the optimized results of irrigation water use under uncertainty were obtained. The case study indicated that the developed method can effectively find the highly sensitive key parameters in the optimization model, and can comprehensively consider the impact of uncertainty parameters on the optimization results. The method can greatly reduce the number of uncertainty parameters to be considered in an optimization model, which reduced the model complexity and effectively improved the efficiency and accuracy of the model. The study can provide important scientific reference and practical methods for the optimal allocation of water resources in irrigated areas.