Non-axisymmetric endwall profiling (NAEP) has been widely utilized in reducing secondary flow loss of turbines. However, most of NAEP are designed for the endwall of the entire blade passage, which presents challenges to the design of cooling structures of turbine endwall. This study aims to explore the local concave non-axisymmetric endwall profiling (LCNP) method with the same effect as whole passage NAEP, and reveal the influence mechanism of LCNP on endwall secondary flow structures of a highly-loaded turbine cascade. Under the condition that the maximum depth of LCNP is unchanged, the axial length effect and pitchwise location effect of LCNP are studied, the influence mechanism of LCNP on the secondary flow loss is analyzed, and the genetic algorithm is utilized to optimize LCNP at the optimal position. Results show that as the axial length of the LCNP increases and the pitchwise location gets closer to the suction surface, the intensity and range of the passage vortex are decreased, and the total pressure loss coefficient (loss coefficient) of the turbine cascade is decreased. When LCNP is 100% axial chord in length and at the position of 2/9 pitch, the loss coefficient is reduced by 5.49%. LCNP was optimized at the optimal position, and the optimal LCNP reduced the loss coefficient of the turbine cascade by 6.73%. After the local concave endwall profiling, the loading in the middle of the endwall of the turbine cascade is reduced, and the intensity of the passage vortex is effectively inhibited, which is the mechanism that the loss coefficient of the turbine cascade is reduced. However, after the local concave endwall profiling, the loading in the trailing of the endwall of the turbine cascade is increased, the transverse migration of the new boundary layer in the endwall is accelerated, and the loss coefficient of the near endwall is increased.