Endometrial endometrioid carcinoma is usually divided into three histological subgroups: grade 1 (G1), grade 2 (G2), and grade 3 (G3). Most cases of endometrial endometrioid carcinoma G1/2 have a favorable prognosis, although some can have unfavorable outcomes, especially when they involve elderly patients, with similarities to endometrioid carcinoma G3 and serous carcinoma. This retrospective study evaluated whether TP53 abnormalities in endometrial endometrioid carcinoma could be used to supplement the current grading system and improve its ability to predict clinical outcomes. Immunohistochemical expression of TP53 was analyzed using tissue microarrays from the surgically resected specimens of 475 patients with endometrial endometrioid carcinoma. Weak or moderate expression was defined as TP53-normal expression, while absent or strongly positive expression was defined as TP53-aberrant expression. The endometrial endometrioid carcinomas had originally been diagnosed as G1 (69%), G2 (18%), and G3 (13%). Univariate analyses revealed that TP53-aberrant expression was associated with poor survival in G1 and G2 cases, but not G3 cases. In addition, age (<60 years vs. ≥60 years) was correlated with TP53-aberrant expression in G1 cases (3% vs. 16%, p = 0.001), but not in G2 or G3 cases. Based on immunohistochemical TP53 expression, the endometrial endometrioid carcinomas were reclassified using a prognostic grading system as high-grade (G1 or G2 with TP53- aberrant expression, and G3 with TP53-normal or -aberrant expression) or low-grade (G1 or G2 with TP53-normal expression). The multivariate analyses revealed that the prognostic grading system (using histological grade and TP53 expression) could independently predict poor progression-free survival (hazard ratio: 2.91, p < 0.001) and overall survival (hazard ratio: 3.62, p < 0.001). Therefore, combining immunohistochemical TP53 expression with the traditional histological grading system for endometrial endometrioid carcinoma may help improve its ability to accurately predict the patient's prognosis.