Automatic workflow generation is becoming an active research area for dealing with the dynamics of grid infrastructure, because it has a pervasive impact on system usability, flexibility and robustness. Artificial intelligence technology and explicit knowledge have been exploited in some research for workflow construction or composition. With the increasing use of knowledge, its quality has growing impact on system performance. In this report, we present the process pattern as a vehicle for knowledge representation to capture process expertise at the business level. A pattern-based planning approach is proposed for automated workflow generation. Our pattern-oriented approach decreases user-visible complexity and makes systems more scalable and flexible by utilizing explicit knowledge support. Then we propose a hybrid method of pattern knowledge optimization for pattern-based workflow generation planning; experts define the primary model, and subsequent classifier training adjusts and improves the pattern knowledge settings. Experiments with a prototype application demonstrated that this approach can substantially reduce modelling difficulties and effectively improve pattern knowledge quality.