Abstract: The variations in coal seam thickness and coal quality parameters (i.e., calorific value, volatile matter, ash content, and sulfur content) determine the spatial distribution of coal prices. The accurate characterization of coal seams’ economic value plays an important role in the rational mining and utilization of coal resources. This study first determined that the geological attributes of coal seams including their thickness and coal quality are regional variables. Based on this, this study discretized geological bodies in the study area using a block model. Then, using a geostatistical method, this study obtained the experimental semivariograms of coal seam thickness and coal quality measured from geological boreholes. The spatial evaluation was performed using the Ordinary Kriging method when coal seams featured small variations and abundant measured data and it was possible to construct a mathematical model of the experimental semivariograms within the variation ranges. Otherwise, the spatial estimation was conducted using the inverse distance weighted (IDW) method, and the optimized power with the smallest root mean square error was obtained using the cross-validation method. Subsequently, the coal price of each block was calculated using the steam coal pricing method, and the block model for coal seam economic value was established. Finally, taking the No. 5 coal seam of the Madiliang coal mine in the Jungar coalfield as an example, this study optimized the mining sequence of several mining faces considering the heterogenous block model of coal seam economic value. The results indicated that the spatial distribution of coal prices exhibited significant heterogeneity. Specifically, the coal prices ranged from 574 yuan/t to 1192 yuan/t (average: 934 yuan/t), showing a normal distribution. The net present value (NPV) of shales increased by 416 million yuan, i.e., 1.64%, after the mining sequence was optimized, indicating a significant increase in the mining benefit. The geostatistical method can fully explore the spatial correlations of measured data. The block models of coal seam thickness, coal quality, and coal prices established using this method yielded macroscopic statistics. Moreover, the geostatistical method allows for fine-scale spatial mapping of those coal seam properties. Therefore, this method can be widely applied in the subsequent coal dressing, coal blending, and coal sales. The block models of coal seams established using the geostatistical method, whose accuracy is influenced by the measured data amount, can be further dynamically corrected and predicted using newly generated data in the process of mining.