Renewable energy has been interested due to the fossil energy depletion and the carbon dioxide reduction. Bio-diesel is one of the most desirable renewable energy because it can alternate the diesel from petroleum directly. However, the bio-diesel using soybean, canola, corn crop, etc. can be confronted with food consumption. Because a microalgae has higher oil lipid contents and rapider growth rates comparing to the earlier raw materials, it can be a good source for bio-diesel production. Because pond production system is limited in distinguishable four seasons and insufficient land availability in Korea, photo-bioreactor (PBR) is a good alternative way to cultivate microalgae by artificially controlling the internal environments such as light, nutrients, temperature, carbon dioxide, and so on. Despite the availability of these PBRs, only few of them can be practically used for mass production. In this study, computational fluid dynamics (CFD) was used to find an optimum bubble-column PBRs for mass cultivation of the microalgae. Multi-phase models including bubble movement, meshes & time step independent tests were used to design the three dimensional CFD model. The model was validated comparing to the field experiments including particle image velocimetry (PIV) tests. Using the validated CFD model, various types of PBRs were compared quantitatively with consideration of a microalgae growth model adaptable for the CFD model. An evaluation method for mixing efficiency in the PBRs was developed by tracing the movement of each particle in the PBRs connected with the growth model according to the light intensity. The results showed that the mixing efficiency and uniformity in the 20 L of plate type PBRs can be enhanced by 73% and 36% respectively when the baffle was installed in order to guide the fluid flow using bubble injection.