Multiple technology projects in a group are evaluated by several experts. Each project covers certain fields and each expert has his/her own advantageous fields. Thus, it is a significant challenge to scientifically and automatically group experts in suitable fields of relevant projects from a large number of candidates. This study proposes a multi-match model GIS of evaluation experts for technology projects based on the greedy algorithm. This model is applied to the two associated “project-field” and “expert-field” correlation matrices. Specifically, it separately calculates the discrete distribution of projects and experts in each field. Then it uses a proper evaluation function to measure the project-expert match and finally obtains the optimal team. The experiments based on the data sets in the power industry show that this model can match the experts with the technology projects and thus has high rationality and accuracy. It avoids the careless mistakes and unfairness in traditional expert selection while reducing human costs.